GPT-5.6 Sol vs Gemini 3.1 Pro: Two Flagships in a Dead Heat (2026)

GPT-5.6 Sol and Gemini 3.1 Pro are the two frontier models compared here. GPT-5.6 Sol is OpenAI's flagship capability tier, generally available July 9, 2026, priced at $5 per million input tokens and $30 per million output tokens with a 1,050,000-token context window and text-and-image input. Gemini 3.1 Pro is Google DeepMind's flagship, priced at $2 per million input tokens and $12 per million output for prompts up to 200,000 tokens, with a 1,000,000-token context window and native text, image, video, audio, and PDF input. On LMArena, the one independent leaderboard that scores both, they sit one Elo point apart at 1486 to 1485. GPT-5.6 Sol leads on the Artificial Analysis Coding Agent Index (No.1 at 80, where Gemini is not charted), output length (128,000 tokens against 64,000), and a newer knowledge cutoff. Gemini 3.1 Pro leads on price (less than half at the standard tier), native multimodal input, and Google ecosystem grounding. Best for verified capability leaderboards and long output: GPT-5.6 Sol. Best for price and multimodal breadth: Gemini 3.1 Pro.

Quick Verdict

This is a split verdict between two flagships human voters can barely tell apart: on LMArena they are one Elo point apart, and from there the two vendors compete on different axes — Sol on capability leaderboards and output, Gemini 3.1 Pro on price and modality. GPT-5.6 Sol went generally available on July 9, 2026, two days before this comparison; Gemini 3.1 Pro has been Google's deployed flagship since February 2026 and sits in our production stack through Google AI Studio and Vertex AI. We have API access to both and have run both side-by-side, so we scope Sol's hands-on claims to roughly 48 hours and lean on attributed third-party benchmarks — Artificial Analysis, LMArena, and vals.ai — wherever our own time is too short. Every figure below carries its source, and self-reported vendor numbers are labeled as such. Here is the short version.

• Best on human preference: effectively a tie. On LMArena, GPT-5.6 Sol's Xhigh configuration sits at 1486 Elo and Gemini 3.1 Pro at 1485 — a single point, well inside the margin where results flip. Neither wins blind human voting in any meaningful sense.
• Best on the independent agentic-coding leaderboard: GPT-5.6 Sol. Artificial Analysis ranks it No.1 on the Coding Agent Index at 80; Gemini 3.1 Pro is not separately charted on that board, so Sol has the stronger independent agentic-coding signal here.
• Best for price: Gemini 3.1 Pro, decisively. At the standard tier it costs $2 per million input tokens and $12 output for prompts up to 200,000 tokens, against Sol's flat $5 and $30 — less than half on both sides. Both rate cards are vendor-verified.
• Best for multimodal input: Gemini 3.1 Pro. It accepts native text, image, video, audio, and PDF input; GPT-5.6 Sol takes only text and image. For video and audio understanding in a single call, Gemini is the only option of the two.
• Best for long output: GPT-5.6 Sol. Its 128,000-token output ceiling is double Gemini 3.1 Pro's 64,000, so single very long replies hit the ceiling later on Sol.
• Best for the newest world knowledge: GPT-5.6 Sol. Its knowledge cutoff is February 16, 2026 against Gemini's January 2025 — roughly thirteen months fresher without grounding enabled.
• Best for the Google ecosystem: Gemini 3.1 Pro. Native Google Search and Maps grounding plus first-party distribution across AI Studio, Vertex AI, the CLI, Android Studio, and Antigravity is unmatched by Sol.
• Best for GA contract stability: GPT-5.6 Sol. It is generally available, whereas Gemini 3.1 Pro still carries Google's Preview label, which has a documented shutdown precedent.

The honest caveats up front: Sol has been public for two days, so we treat our hands-on notes as first impressions, not a settled verdict. Neither model has an independently verified SWE-bench score — Sol has not been submitted, and Gemini's 80.6 percent is self-reported on DeepMind's model card, not run by vals.ai. The Artificial Analysis Intelligence Index figures (59 for Sol, 57 for Gemini) were measured on different index snapshots, so their two-point gap is not decisive. We only declare a winner where both models were measured on the same independent benchmark, and we keep self-reported and third-party numbers strictly apart.

GPT-5.6 Sol vs Gemini 3.1 Pro — Overview

What Is GPT-5.6 Sol?

GPT-5.6 Sol is the flagship capability tier of OpenAI's GPT-5.6 generation, generally available July 9, 2026 after a gated preview on June 26. In OpenAI's naming scheme the number is the generation and the names — Sol, Terra, and Luna — are durable capability tiers rather than sizes; Sol is the tier aimed at the hardest problems, from complex coding and long-horizon agents to cyber, science, and computer use, per OpenAI's announcement. Its predecessor GPT-5.5 remains active and cheaper for routine work — see our GPT-5.5 review for that tier. Per OpenAI's model documentation, Sol runs a 1,050,000-token context window with up to 128,000 output tokens and a February 16, 2026 knowledge cutoff, handles text and image inputs to text output, and introduces two new reasoning levels above xhigh: max, and ultra, a multi-agent mode that runs up to sixteen reasoning agents in parallel. It also adds Programmatic Tool Calling, where the large language model writes and executes JavaScript in an isolated, ephemeral runtime to orchestrate its own tools. API pricing is a flat $5 per million input tokens and $30 per million output tokens, with cached input at $0.50 per million and no long-context surcharge.

What Is Gemini 3.1 Pro?

Gemini 3.1 Pro is Google DeepMind's flagship Gemini 3 series model, announced February 19, 2026 and still carrying the Preview label on its gemini-3.1-pro-preview model ID as of July 2026, even as it powers Google's frontier developer surfaces. We review it in depth in our Gemini 3.1 Pro review (our score: 9.0 out of 10). Per Google's model documentation and DeepMind's model card, it runs a 1,000,000-token input context with up to 64,000 output tokens and a January 2025 knowledge cutoff, and it accepts native multimodal input across text, image, video, audio, and PDF, returning text. It uses adaptive thinking shaped by an effort dial rather than an explicit extended-thinking flag, ships native Google Search and Maps grounding as first-party tools, and offers the widest first-party distribution of any frontier vendor — Google AI Studio, Vertex AI, the Gemini API and app, the Gemini CLI, Android Studio, and Google Antigravity. API pricing uses context-length tiering, which we verified directly on Google's pricing page: $2 per million input tokens and $12 output for prompts up to 200,000 tokens, rising to $4 and $18 above that. For the faster, cheaper sibling model, see our Gemini 3 Flash review.

How We Compared Them — and What We Did Not Do

Method transparency matters more than usual here, because Sol is two days old at the time of writing and the two vendors publish different benchmarks that are easy to conflate. Here is exactly what we did and did not do.

• Pricing: both rate cards are vendor-verified at the source. Sol's flat $5 input and $30 output per million tokens is confirmed against OpenAI's API documentation; Gemini 3.1 Pro's tiered $2 and $12 (up to 200,000 tokens) is confirmed against Google's pricing page, including the higher $4 and $18 band above 200,000 tokens. No relayed figures.
• Independent benchmarks: we lean on LMArena (Elo) and Artificial Analysis (Intelligence Index and Coding Agent Index). We only declare a benchmark winner where both models were measured on the same suite under consistent conditions. LMArena is the one board that ranks both — Sol at 1486, Gemini at 1485 — so it anchors the head-to-head.
• Data gaps we flag rather than fill: neither model appears on the independent vals.ai SWE-bench Verified board. Sol has not been submitted; Gemini's 80.6 percent is self-reported on DeepMind's model card. Gemini is not separately charted on the Artificial Analysis Coding Agent Index that ranks Sol No.1. We say so plainly and do not substitute one vendor's number for the other's missing one.
• Self-reported figures: OpenAI's Terminal-Bench 2.1, DeepSWE, and SWE-bench Pro numbers for Sol, and DeepMind's GPQA Diamond, ARC-AGI-2, and SWE-bench Verified numbers for Gemini, are labeled as vendor-reported and not treated as head-to-head evidence. Our agentic coding model explainer covers why these agentic scores are not interchangeable across suites.
• Hands-on: we have run Gemini 3.1 Pro through Google AI Studio and Vertex AI on our content workflow since April 2026, and Sol for roughly 48 hours since its July 9 GA, side-by-side on the same tasks. That is enough for first impressions on Sol, not a controlled benchmark, and we scope every observation accordingly.
• Disclosure: we have no affiliate relationship with OpenAI or Google. There are no sponsored links on this page. Our team uses Gemini 3.1 Pro in production, which is exactly why we have held this comparison to independent, attributed numbers rather than our own habit.

Features and Benchmarks Comparison

The table below lists every dimension we could verify or attribute. Read the Winner column carefully: it distinguishes vendor-verified pricing, independent benchmarks, and self-reported figures, and it flags where a result is one-sided or genuinely tied. Every benchmark figure carries its source. Sources for the independent scores are Artificial Analysis and LMArena.

Synthesis: the honest headline is a dead heat. On LMArena, the only board that ranks both under identical conditions, one Elo point separates them (1486 to 1485), and on the Artificial Analysis Intelligence Index they are two points apart on different snapshots (59 to 57) — statistically indistinguishable either way. From there the models diverge by design, not by overall quality. Gemini 3.1 Pro owns price (less than half at the standard tier), native video and audio input, and Google-native grounding. GPT-5.6 Sol owns the independent Coding Agent Index (No.1 at 80, where Gemini is not charted), double the output ceiling, the newer knowledge cutoff, an ultra multi-agent mode, and GA status. The SWE-bench numbers everyone reaches for do not settle it: neither model has an independently verified score, so we treat that row as a wash. This is not a model that wins everything against a model that wins nothing; it is two near-identical flagships optimized for different buyers.

Pricing — GPT-5.6 Sol vs Gemini 3.1 Pro in 2026

Pricing is where these two flagships separate most clearly, and it favors Gemini 3.1 Pro. Sol is flat and simple; Gemini is tiered and cheaper on almost every realistic prompt. The question is not whether Gemini is cheaper — it is, on both input and output — but whether Sol's capability and output advantages justify the premium on your workload. For the mechanics of input, output, and cached-token billing, our AI model pricing explainer breaks down how these rate cards translate into real bills. Both rate cards below come straight from OpenAI's and Google's own documentation.

GPT-5.6 Sol Pricing

Context pricing is flat — there is no long-context surcharge for Sol, so a 900,000-token request bills at the same per-token rate as a short one. There is no free plan at the API level.

Gemini 3.1 Pro Pricing

Gemini 3.1 Pro has no free tier on the paid API — free access is limited to interactive testing in Google AI Studio. Its native Google Search and Maps grounding tools are billed at 5,000 prompts per month free, pooled across the Gemini 3 family, then $14 per 1,000 queries.

Pricing verdict: Gemini 3.1 Pro wins on price on almost any realistic prompt. On a representative agentic call of 50,000 input tokens and 5,000 output tokens, Sol costs about $0.40 at the rate card ($5 times 0.05 input plus $30 times 0.005 output), while Gemini in its standard band costs about $0.16 ($2 times 0.05 plus $12 times 0.005) — roughly 60 percent cheaper on that mix. The one place the gap narrows is very large prompts: above 200,000 tokens Gemini rises to $4 input and $18 output, still cheaper than Sol's flat $5 and $30 but by a smaller margin. Both models discount cached input by roughly 90 percent, so long-running agents with stable system prompts close some of the raw-rate gap on both sides. Sol's counter is not price but what the price buys — double the output ceiling and the No.1 Coding Agent Index placement — so the pricing question folds into the capability question rather than standing alone.

Hands-On Notes — Gemini 3.1 Pro in Production, Sol at 48 Hours

We owe you precision about what this section is and is not. Gemini 3.1 Pro has been in our content workflow since April 2026, running through Google AI Studio and Vertex AI for grounding, multimodal extraction, and long-context research. GPT-5.6 Sol went GA on July 9; we had it running side-by-side within hours, which gives us roughly 48 hours of direct comparison at the time of writing — sharp first impressions, nowhere near a controlled benchmark. Take the Sol observations as scoped and provisional, and weight the attributed benchmarks above them.

Where Gemini 3.1 Pro stood out: multimodal input and price. Dropping a short screen-recording plus a slide deck into a single prompt and getting timestamped output is something Sol structurally cannot do, because Sol takes only text and image input. On high-volume fact-checking and extraction runs, Gemini's lower rate card made bulk work genuinely cheap, and native Google Search grounding returned sourced answers without our building a retrieval pipeline. This lines up with its price advantage and multimodal stack without proving anything about peak reasoning in 48 hours.

Where Sol stood out immediately: long output and reasoning control. On a long-form generation task that needed a single very large reply, Sol's 128,000-token output ceiling cleared the job where Gemini's 64,000 would have forced a second pass. Sol's lower reasoning levels made cheap bulk calls cheap, and its ultra mode — multiple reasoning agents in parallel — produced visibly more thorough plans on one hard architecture task than its standard mode, at a higher token bill for that call. This is consistent with its No.1 Coding Agent Index placement, without proving it over two days.

Where they felt interchangeable: everyday reasoning and structured extraction. On routine transforms, summarization, and JSON-schema extraction, the outcome gap between the two was small to invisible in our runs — which is exactly what a one-Elo-point LMArena gap predicts. On that broad middle of the workload, the deciding factor was price, and that points to Gemini.

What we cannot tell you yet: latency under controlled conditions, per-task token economics across a real workload mix, and whether Sol's early production behavior holds up over weeks. We will update this comparison as our side-by-side time with Sol accumulates and as more independent harnesses publish results for both models.

Winner per Category

Best for Human Preference: Effectively a Tie

On LMArena, where anonymized models compete in blind human voting, GPT-5.6 Sol's Xhigh configuration holds 1486 Elo and Gemini 3.1 Pro sits at 1485 — one point apart, deep inside the range where results routinely flip between refreshes. Blind preference captures tone, helpfulness, and formatting as much as correctness, and on that signal these two are the closest frontier pairing we have compared. There is no honest winner here; anyone claiming one model is clearly preferred by users over the other is reading noise as signal. For chat-facing and assistant workloads, pick on price or modality, not on this leaderboard.

Best on the Independent Agentic-Coding Leaderboard: GPT-5.6 Sol

The Artificial Analysis Coding Agent Index is the one independent capability chart where these two are not level: it ranks GPT-5.6 Sol No.1 at 80, while Gemini 3.1 Pro is not separately charted on it. That is a genuine edge for Sol on independent agentic coding, though we flag the asymmetry honestly — this is Sol leading a board Gemini is absent from, not Sol beating a charted Gemini score. Sol also owns multi-agent throughput outright: its ultra mode runs up to sixteen reasoning agents in parallel, a mode Gemini does not offer, and its Programmatic Tool Calling orchestrates tools in executable code. For long-horizon agentic pipelines measured on the Coding Agent Index, Sol is the pick on the independent evidence. Our agentic coding explainer covers why these agent benchmarks differ from chat quality.

Best for Price: Gemini 3.1 Pro

This one is not close. At the standard tier for prompts up to 200,000 tokens, Gemini 3.1 Pro costs $2 per million input tokens against Sol's $5, and $12 output against $30 — less than half on both sides, both vendor-verified. Batch mode halves Gemini again to $1 input and $6 output. Even in its higher context band above 200,000 tokens ($4 and $18), Gemini stays cheaper than Sol's flat $5 and $30. Unless your tasks demonstrably need Sol's output length or Coding Agent Index edge, Gemini delivers substantially more output per dollar — which is why, on the broad middle of routine work where the two feel interchangeable, price makes Gemini the rational default.

Best for Multimodal Input and the Google Ecosystem: Gemini 3.1 Pro

Gemini 3.1 Pro accepts native text, image, video, audio, and PDF input in a single call; GPT-5.6 Sol accepts only text and image. For any workload involving video understanding, audio transcription-plus-reasoning, or mixed-media extraction, Gemini is the only option of the two that does it natively. It also ships native Google Search and Maps grounding and the deepest first-party distribution of any frontier vendor — AI Studio, Vertex AI, the Gemini CLI, Android Studio, and Google Antigravity — so teams already inside Google Cloud get the shortest path to production. If your stack is Google-native or your inputs are multimodal, this category decides it for Gemini.

Best for Long Output and Newest Knowledge: GPT-5.6 Sol

Sol's 128,000-token output ceiling is double Gemini 3.1 Pro's 64,000, so single very long replies — full-length reports, large code translations — hit the ceiling later on Sol. Its February 16, 2026 knowledge cutoff is roughly thirteen months fresher than Gemini's January 2025, which matters for world-knowledge questions when grounding is off (Gemini's counter is that its native grounding pulls live data on demand). Sol is also generally available, while Gemini 3.1 Pro still carries the Preview label with a documented shutdown precedent — Google retired the previous Gemini 3 Pro Preview on March 9, 2026 with a forced migration. For long-output generation, the newest built-in knowledge, and GA contract stability, Sol has the edge.

Pros and Cons

GPT-5.6 Sol Pros and Cons

What we like about GPT-5.6 Sol

• No.1 on the independent AA Coding Agent Index. A score of 80, where Gemini 3.1 Pro is not separately charted — the one independent capability board where Sol leads outright.
• Double the output ceiling. 128,000 output tokens against Gemini's 64,000, for single very long replies without a second pass.
• Newer knowledge and GA status. A February 2026 cutoff against January 2025, and general availability against Gemini's Preview label.
• Ultra multi-agent mode and Programmatic Tool Calling. Up to sixteen parallel reasoning agents and code-orchestrated tool use, neither of which Gemini offers as a discrete capability.
• Flexible, flat pricing. A five-plus-level reasoning scale you can turn down for cheap bulk calls, and no long-context surcharge on large prompts.

Where GPT-5.6 Sol falls short

• More than double the price of Gemini on input. $5 against $2 input and $30 against $12 output per million tokens at the standard tier.
• Text and image input only. No native video or audio input, where Gemini accepts both — a hard gap for multimodal workloads.
• No independent SWE-bench Verified score. Not submitted, so its strongest coding evidence is the Coding Agent Index and self-reported Terminal-Bench figures.
• Two days old at the time of writing. Our hands-on window is roughly 48 hours, so its production behavior over weeks is unproven.
• Shallower first-party ecosystem than Google's. No first-party CLI or IDE integration at Gemini's depth, and no native web-search grounding at Google's scale.

Gemini 3.1 Pro Pros and Cons

What we like about Gemini 3.1 Pro

• Less than half the price at the standard tier. $2 input and $12 output per million tokens against Sol's $5 and $30, with Batch halving it again.
• Native multimodal input. Text, image, video, audio, and PDF in a single call — the clearest capability Sol cannot match.
• Native Google Search and Maps grounding. 5,000 free prompts per month then $14 per 1,000 queries, for sourced answers without a retrieval pipeline.
• Deepest first-party distribution. AI Studio, Vertex AI, Gemini CLI, Android Studio, and Antigravity — the widest integration story of any frontier vendor.
• Level on human preference. One LMArena Elo point behind Sol (1485 to 1486) — statistically a tie at a fraction of the price.

Where Gemini 3.1 Pro falls short

• Half the output ceiling. 64,000 output tokens against Sol's 128,000, so long single replies hit the wall earlier.
• Older knowledge cutoff. January 2025 against Sol's February 2026 — roughly thirteen months behind without grounding enabled.
• Still a Preview model. Google's Preview label carries change-management risk, with a real shutdown precedent from March 2026.
• Not charted on the AA Coding Agent Index. No independent agentic-coding-index number to weigh against Sol's No.1 placement.
• Context-length surcharge and no free API tier. Prompts above 200,000 tokens cost more per token, and there is no free plan on the paid API.

When to Pick GPT-5.6 Sol vs Gemini 3.1 Pro

Pick GPT-5.6 Sol if...

• Your workload is agentic coding measured on the AA Coding Agent Index, where Sol ranks No.1 at 80 and Gemini is not charted.
• You generate long single replies and need the 128,000-token output ceiling rather than Gemini's 64,000.
• You want parallel multi-agent reasoning (ultra, up to sixteen agents) or code-orchestrated tool use via Programmatic Tool Calling.
• The newest built-in world knowledge matters and you cannot always enable grounding — Sol's cutoff is roughly thirteen months fresher.
• You need GA contract stability rather than a Preview model that can change with limited notice.

Pick Gemini 3.1 Pro if...

• Price-performance is the deciding factor — less than half Sol's input and output rates at the standard tier.
• Your inputs are multimodal — native video, audio, and PDF in a single call, which Sol cannot accept.
• You want native Google Search and Maps grounding for sourced, up-to-date answers without building retrieval.
• Your stack is Google-native — AI Studio, Vertex AI, the CLI, Android Studio, or Antigravity give the shortest path to production.
• Human-facing output quality matters and you want it at a fraction of the price, given the one-Elo-point LMArena gap.

Frequently Asked Questions

Is GPT-5.6 Sol better than Gemini 3.1 Pro in 2026?

It depends on what you are optimizing for, and we will not fake a single overall winner. On LMArena, the one independent leaderboard that scores both under identical conditions, they are separated by a single Elo point — 1486 for GPT-5.6 Sol against 1485 for Gemini 3.1 Pro — so human voters can barely tell them apart. Sol leads where capability and freshness matter: it holds No.1 on Artificial Analysis's Coding Agent Index at 80, ships double the output ceiling (128,000 tokens against 64,000), a newer February 2026 knowledge cutoff, and a multi-agent ultra reasoning mode. Gemini 3.1 Pro leads on economics and breadth: it costs less than half at the standard tier ($2 against $5 per million input tokens), accepts native video and audio input that Sol cannot, and plugs into Google Search grounding and the widest first-party distribution of any vendor. Best for verified capability leaderboards and long output: Sol. Best for price and multimodal breadth: Gemini 3.1 Pro.

How much do GPT-5.6 Sol and Gemini 3.1 Pro cost?

GPT-5.6 Sol costs $5 per million input tokens and $30 per million output tokens, with cached input at $0.50 per million and Batch mode at half price — we confirmed this on OpenAI's API documentation. Its pricing is flat, with no long-context surcharge. Gemini 3.1 Pro uses context-length tiering, which we verified directly on Google's pricing page: for prompts up to 200,000 tokens it costs $2 per million input and $12 per million output; above 200,000 tokens it rises to $4 input and $18 output, with cached input at $0.20 and $0.40 per million respectively. Batch and Flex tiers halve those rates. At the standard tier Gemini is less than half of Sol on both input and output, and it stays cheaper even in its higher context band, though the gap narrows on very large prompts. Gemini also has no free tier on the paid API — only interactive testing through Google AI Studio is free.

Which is better for coding: GPT-5.6 Sol or Gemini 3.1 Pro?

It splits by which benchmark you trust, and neither model has an independently verified coding score to settle it cleanly. On Artificial Analysis's Coding Agent Index, GPT-5.6 Sol ranks No.1 at 80, while Gemini 3.1 Pro is not separately charted on that board. On SWE-bench Verified, the independent vals.ai leaderboard lists neither model directly — Sol has not been submitted, and Gemini's widely cited 80.6 percent comes from DeepMind's own model card, not an independent run. So the honest picture is: Sol has the stronger independent agentic-coding signal (No.1 Coding Agent Index), Gemini has a published SWE-bench Verified number but a self-reported one, and OpenAI's own Terminal-Bench 2.1 figure of 88.8 percent for Sol is also self-reported. If you weight independent agentic-coding results, Sol leads; if you weight vendor model-card SWE-bench numbers, Gemini has one and Sol does not.

Why is GPT-5.6 Sol missing from SWE-bench Verified?

Because OpenAI has not submitted GPT-5.6 Sol to it, so as of this comparison there is no independent SWE-bench Verified figure for the model, and we flag that gap rather than invent a number. Gemini 3.1 Pro is not on the independent vals.ai SWE-bench Verified board either — its 80.6 percent score comes from DeepMind's model card, which is self-reported. On the independently run vals.ai leaderboard the top entries are other models, such as Claude Fable 5 at 95 percent and Claude Opus 4.8 at 88.6 percent, neither of which is in this matchup. OpenAI reports Sol on SWE-bench Pro, a different and harder suite, at 64.6 percent, and disputes that benchmark's validity. Comparing a self-reported SWE-bench Pro number against a self-reported SWE-bench Verified number is not a like-for-like comparison, so we keep them separate and lean on the Coding Agent Index and LMArena, where the models are measured under consistent, independent conditions.

Is Gemini 3.1 Pro really half the price of GPT-5.6 Sol?

At the standard tier for prompts up to 200,000 tokens, yes — and we verified both rate cards at the source. Gemini 3.1 Pro costs $2 per million input tokens and $12 per million output, against Sol's flat $5 input and $30 output, so it is roughly 60 percent cheaper on input and 60 percent cheaper on output in that band. The nuance is Gemini's context-length tiering: for prompts above 200,000 tokens the rate rises to $4 input and $18 output per million, which narrows but does not erase the gap — Gemini is still cheaper than Sol's flat $5 and $30 even at the higher band. Sol's pricing, by contrast, never changes with prompt size. Both models discount cached input by roughly 90 percent, and both offer half-price batch modes. For high-volume or cost-sensitive workloads, Gemini 3.1 Pro is the clearly cheaper of the two on almost any realistic prompt shape.

Which has the larger context window: GPT-5.6 Sol or Gemini 3.1 Pro?

GPT-5.6 Sol edges it on both context and output, though the context difference is small. OpenAI's model documentation lists Sol at a 1,050,000-token input context with up to 128,000 output tokens and a February 16, 2026 knowledge cutoff. Google's documentation lists Gemini 3.1 Pro at a 1,000,000-token input context with 64,000 output tokens and a January 2025 knowledge cutoff. The 5 percent context difference rarely changes an architecture decision — both handle book-length inputs and large multi-file codebases. The output ceiling is the more meaningful gap: Sol's 128,000 output tokens is double Gemini's 64,000, so tasks that need a single very long reply, such as full-length report generation or large code translations, hit the ceiling earlier on Gemini. Gemini's answer is its context caching and grounding, which reduce the need to regenerate long outputs from scratch.

What can Gemini 3.1 Pro do that GPT-5.6 Sol cannot?

The clearest capability gap is native multimodal input. Gemini 3.1 Pro accepts text, image, video, audio, and PDF in a single call and returns text, which lets you drop a video walkthrough plus a slide deck into one prompt and get timestamped output. GPT-5.6 Sol accepts only text and image input to text output — image generation and other modalities are separate callable tools rather than native inputs. Gemini also ships native Google Search and Maps grounding as first-party tools (5,000 prompts per month free across the Gemini 3 family, then $14 per 1,000 queries), and it has the deepest first-party distribution of any frontier vendor, spanning Google AI Studio, Vertex AI, the Gemini CLI, Android Studio, and Google Antigravity. If your workload is multimodal extraction, retrieval-grounded answering, or anything embedded in the Google Cloud stack, those are real Gemini advantages Sol does not match.

What can GPT-5.6 Sol do that Gemini 3.1 Pro cannot?

Sol's differentiators are output length, reasoning control, and general availability. Its 128,000-token output ceiling is double Gemini's 64,000, so it can return much longer single replies. Its reasoning scale runs from low through xhigh, then adds a new max level and an ultra mode that spawns up to sixteen reasoning agents in parallel to attack one problem — Gemini uses adaptive thinking shaped by an effort dial, with no equivalent discrete multi-agent mode. Sol also introduces Programmatic Tool Calling, where the model writes and executes JavaScript in an isolated, ephemeral runtime to orchestrate its own tools, which is documented as compatible with zero-data-retention deployments. And Sol is generally available as of July 9, 2026, whereas Gemini 3.1 Pro still carries Google's Preview label. For long-output generation, parallel multi-agent reasoning, and GA contract stability, Sol has the edge.

What is GPT-5.6 Sol's ultra reasoning mode?

Ultra is a new multi-agent reasoning setting introduced with the GPT-5.6 generation, and it is primarily a Sol feature. Per OpenAI's documentation, the reasoning effort scale now runs from low through xhigh, then adds a new max level and, above that, ultra — which spawns multiple reasoning agents in parallel, four by default and up to sixteen, to attack a single problem. It is aimed at the hardest long-horizon coding, science, and agentic tasks, and OpenAI reports Sol at 91.9 percent on Terminal-Bench 2.1 in ultra mode against 88.8 percent standard, both self-reported. Gemini 3.1 Pro has no equivalent discrete mode; it uses adaptive thinking that you shape with an effort parameter rather than parallel agents. Ultra buys headroom on Sol's hardest tasks at a higher token cost per call, so it is a lever you reach for on specific problems, not a default.

Which model is in general availability, and does Preview status matter?

GPT-5.6 Sol reached general availability on July 9, 2026, after a gated preview on June 26. Gemini 3.1 Pro still carries Google's Preview label, though it has been widely deployed since its February 19, 2026 announcement and powers Google's flagship developer surfaces. Preview status matters for production contracts: pricing, rate limits, model IDs, and response shapes can change with limited notice, and Google has a real precedent here — it shut down the previous Gemini 3 Pro Preview on March 9, 2026 with a forced migration to 3.1. That is a reason to code defensive fallbacks if you build on Gemini 3.1 Pro for mission-critical workloads. In practice the Preview label is not a stability warning about output quality — the model is battle-tested — but a contractual caveat about change management that a GA model like Sol does not carry.

Can GPT-5.6 Sol and Gemini 3.1 Pro work together in the same stack?

Yes, and a split stack is a rational setup given how evenly they are matched. A practical routing pattern sends long-output generation, parallel multi-agent reasoning, and agentic coding measured on the Coding Agent Index to GPT-5.6 Sol, and sends cost-sensitive, high-volume, multimodal, or Google-grounded work to Gemini 3.1 Pro, where it is cheaper and accepts native video and audio. Abstraction layers such as the Vercel AI SDK, LangChain, or LiteLLM turn cost-and-capability routing by task type into a configuration exercise rather than a rewrite. Because the two sit one Elo point apart on human preference, the deciding factors are usually price, modality, output length, and ecosystem rather than raw quality — which makes them complementary. Many teams already run one of each and route by workload, and given the price gap, defaulting cost-tolerant work to Gemini while reserving Sol for its specific strengths is a defensible cost strategy.

What are the alternatives to GPT-5.6 Sol and Gemini 3.1 Pro?

Several frontier models sit close by. Claude Opus 4.8, at $5 per million input and $25 per million output tokens, posts 88.6 percent on the independent SWE-bench Verified suite and is a strong coding-focused middle option — we compare it against Gemini 3.1 Pro directly. Claude Fable 5 is the current No.1 on both the Artificial Analysis Intelligence Index and LMArena, for teams that want peak measured capability regardless of price. GPT-5.5, OpenAI's prior flagship, remains active and cheaper for routine work. On the Google side, Gemini 3 Flash is the faster, cheaper sibling for high-volume tasks that do not need the Pro tier. If you want the adjacent trade-offs, our Claude Opus 4.8 versus Gemini 3.1 Pro and Claude Fable 5 versus Gemini 3.1 Pro comparisons cover the models one step away from this matchup.

Final Verdict — A Dead Heat That Splits by Buyer

After running both side-by-side, verifying pricing on both vendors' own documentation, and holding every capability claim to independent benchmarks, our verdict is a genuine split — and it starts from a dead heat. On the one independent leaderboard that scores both, GPT-5.6 Sol and Gemini 3.1 Pro are one Elo point apart (1486 to 1485 on LMArena), and two points apart on the Artificial Analysis Intelligence Index measured on different snapshots (59 to 57). Human voters and aggregate intelligence scores cannot separate them. So the decision is not "which is better" but "which is better for you," and that splits cleanly. GPT-5.6 Sol is the capability-and-output pick: No.1 on the independent Coding Agent Index at 80, double the output ceiling at 128,000 tokens, a newer February 2026 knowledge cutoff, an ultra multi-agent mode, and GA stability. Gemini 3.1 Pro is the price-and-modality pick: less than half the standard-tier rate on both input and output, native video and audio input Sol cannot match, and the deepest Google-native grounding and distribution. We disclose plainly that our team runs Gemini 3.1 Pro in production — which is exactly why we anchored every capability comparison to third-party numbers rather than our own habit.

We did not crown a single overall winner because the evidence does not support one honestly: the two are statistically level where they are measured together, and each leads on axes the other cannot easily answer. Neither has an independently verified SWE-bench score, so that argument is a wash. If your work is agentic coding, long-output generation, or anything needing the freshest knowledge and GA stability — pick GPT-5.6 Sol. If your work is cost-sensitive, multimodal, or Google-native — pick Gemini 3.1 Pro and bank the price difference. For most teams the rational endgame is routing by workload, because the quality gap is small enough that price, modality, and output length decide it. For the models one step away from this matchup, see our Gemini 3.1 Pro review, our Claude Opus 4.8 review — the coding-focused middle option at $5 input and $25 output per million tokens — our Claude Opus 4.8 vs Gemini 3.1 Pro comparison, our Claude Fable 5 vs Gemini 3.1 Pro comparison, and our Claude Sonnet 5 vs Gemini 3.1 Pro comparison.

Sources

Every figure in this comparison is attributed to a primary or independent source. Pricing and specifications come from the vendors' own documentation; capability scores come from independent third parties; self-reported figures are labeled as such throughout.

• OpenAI — GPT-5.6 announcement and positioning
• OpenAI — GPT-5.6 Sol model documentation and API pricing
• Google — Gemini API pricing (context-length tiers, verified)
• Google — Gemini 3.1 Pro model documentation
• Google DeepMind — Gemini 3.1 Pro model card
• Artificial Analysis — Intelligence Index and Coding Agent Index
• LMArena — human-preference Elo leaderboard
• vals.ai — SWE-bench Verified independent leaderboard

Last compared: July 2026. GPT-5.6 Sol reached general availability on July 9, 2026; Gemini 3.1 Pro has been Google's deployed flagship since February 2026 and still carries a Preview label. Both models are moving fast, and we will revise this comparison as independent benchmark coverage matures.