GPT-5.6 Sol vs DeepSeek V4: Top Intelligence vs Open-Weight Price (2026)

GPT-5.6 Sol and DeepSeek V4 are the two large language models compared here, and they sit at opposite ends of the same frontier. GPT-5.6 Sol is OpenAI's top-capability tier, generally available July 9, 2026, priced at 5 dollars per million input tokens and 30 dollars per million output tokens. DeepSeek V4 is DeepSeek's open-weight Chinese flagship, shipped under an MIT license on Hugging Face, with a hosted V4-Pro tier at 0.435 dollars input and 0.87 dollars output per million tokens and an even cheaper V4-Flash tier. On the one independent evaluator that scores both models the same way, Artificial Analysis, GPT-5.6 Sol leads the Intelligence Index 59 to 44 for DeepSeek V4-Pro, and Sol is the only one of the two with a charted coding score, ranking first on the AA Coding Agent Index at 80. DeepSeek V4 is roughly 11 times cheaper on input and about 34 times cheaper on output, ships open weights you can self-host for full data sovereignty, and matches Sol on context. This is a split verdict, not a single winner. Best for peak measured intelligence, charted coding, and Western data residency: GPT-5.6 Sol. Best for cost, open weights, and self-hosting: DeepSeek V4.

Quick Verdict

This is a split verdict by use case, not a single overall winner. We ran both models side-by-side through their hosted APIs, pulled the pricing directly from each vendor's own pages, and added our own hands-on observations from using both on coding and reasoning prompts. We have not run weeks of controlled, identical-task benchmarking of the two against each other, so where we lean on numbers we attribute them to their source. The honest summary is that these two models are not really fighting for the same buyer. Here is the short version.

• Best for peak measured intelligence: GPT-5.6 Sol. On the Artificial Analysis Intelligence Index — the one composite that scores both models with the same battery — Sol sits at 59 while DeepSeek V4-Pro in its maximum reasoning mode scores 44, a clear 15-point lead.
• Best for measured coding: GPT-5.6 Sol. It ranks first on the Artificial Analysis Coding Agent Index at 80, the top charted score; DeepSeek V4 is not placed on that independent leaderboard at all. Sol also ships a full agentic tool stack — function calling, web search, file search, code interpreter, computer use, and MCP — on by default.
• Best for cost: DeepSeek V4, and it is not close. V4-Pro output at 0.87 dollars per million tokens is roughly 34 times cheaper than Sol at 30 dollars, and V4-Flash output at 0.28 dollars is over 100 times cheaper. On input, V4-Pro at 0.435 dollars is about 11 times cheaper than Sol at 5 dollars.
• Best for open weights and self-hosting: DeepSeek V4. The weights ship under an MIT license and run on your own hardware, including Huawei Ascend chips. Sol is closed and API-only.
• Best for Western data residency and compliance: GPT-5.6 Sol. It is hosted by OpenAI in the US with regional data-residency endpoints. DeepSeek's hosted API runs in China, which is a non-starter for many regulated buyers unless they self-host the open weights.

Bottom line: if you need the strongest measured model, the top charted coding score, or US-hosted compliance, pick GPT-5.6 Sol. If you are cost-constrained, want to own your weights, or need to self-host for data sovereignty, DeepSeek V4 gives you frontier-adjacent quality at a fraction of the price. We did not crown a single winner because the two models optimize for different things, and the numbers back both stories at once: a wide capability gap and a wider price gap.

At a Glance

Before the detail, here is the side-by-side that frames everything below. All pricing in this table was fetched directly from each vendor's pricing page in July 2026. All benchmark figures are attributed to their source, and independent scores are kept strictly separate from vendor-reported ones.

Overview of Each Model

GPT-5.6 Sol

GPT-5.6 Sol is the top tier of OpenAI's GPT-5.6 generation, which became generally available on July 9, 2026 across ChatGPT, Codex, and the API. In the new naming scheme the number is the generation and the names Sol, Terra, and Luna are durable capability tiers rather than model sizes: Sol is the flagship built for the hardest problems — complex coding, long-horizon agentic work, computer use, and science. It carries a 1,050,000-token context window with up to 128K output tokens, a February 16, 2026 knowledge cutoff, and accepts text and image input while producing text output. On independent benchmarks it is the strongest model in this matchup by a clear margin: it tops the Artificial Analysis Intelligence Index at 59 and ranks first on the AA Coding Agent Index at 80. Its defining trait is the agentic tool stack, all on by default — function calling, structured outputs, web search, file search, code interpreter, a hosted shell, computer use, and MCP — alongside a reasoning-effort scale that runs from low through xhigh and adds two new levels, max and a multi-agent ultra mode. Pricing is 5 dollars per million input tokens and 30 dollars per million output, with prompt caching at a 90 percent discount (0.50 dollars per million cached input tokens) and a Batch API at half price. It is closed and available only through OpenAI's surfaces. In our hands-on use, the standout is reliability, tool orchestration, and design judgment rather than a single headline trick. For the full breakdown, see our GPT-5.6 Sol review.

DeepSeek V4

DeepSeek V4 is the Chinese open-weight flagship, shipped April 24, 2026 in two sizes: V4-Pro, a 1.6-trillion-parameter mixture-of-experts model with about 49 billion parameters active per token, and V4-Flash, a 284-billion-parameter model with about 13 billion active. Both carry a 1,000,000-token context window with up to 384K tokens of output, and both ship under an MIT license that permits free commercial use, redistribution, and modification of the weights — although the training code and data recipe are not released, so this is open weights rather than fully open source. Artificial Analysis scores V4-Pro at 44 on its Intelligence Index in maximum reasoning mode, well above the median for open-weight models of similar size, and DeepSeek separately reports 80.6 percent on SWE-bench Verified — a self-reported figure on its own harness, not an independently charted one. The architecture is genuinely novel rather than just bigger: a Hybrid Attention design combining Compressed Sparse Attention at four-times compression with Heavily Compressed Attention at 128-times compression cuts inference compute and KV-cache footprint sharply, and three built-in thinking modes — Non-Think, Think High, and Think Max — let you dial cost against quality per request. It is text only, the hosted API is OpenAI-compatible, and it runs day one on Huawei Ascend hardware. The headline, though, is price: V4-Pro output sits at 0.87 dollars per million tokens and V4-Flash at 0.28 dollars. Our full DeepSeek V4 review covers the architecture and licensing in more depth.

Pricing Compared

This is where the two models diverge most violently, and it is the single most important thing to understand about this matchup. We fetched every number below directly from each vendor's pricing page in July 2026.

Run the arithmetic and the gap is staggering. On output tokens — the comparison most people care about, because output dominates real agentic spend — Sol at 30 dollars is roughly 34 times the cost of V4-Pro at 0.87 dollars, and over 100 times the cost of V4-Flash at 0.28 dollars. On input tokens, Sol at 5 dollars is about 11 times V4-Pro and about 36 times V4-Flash. Even Sol's Batch API discount, which halves the rate card to 2.50 dollars input and 15 dollars output, leaves it an order of magnitude above DeepSeek's hosted pricing. Sol's prompt caching is genuinely cheap by frontier standards at 0.50 dollars per million cached input tokens, but DeepSeek's cache-hit input at 0.003625 dollars for V4-Pro is close to free, so caching does not narrow the gap in DeepSeek's disfavor either.

Two nuances worth flagging honestly. First, the DeepSeek V4-Pro rates above are the discounted tier, which DeepSeek has kept in place as the durable price; V4-Flash is the even cheaper tier for lighter, high-volume work. Both are pay-per-token on a hosted API, and both were read straight off DeepSeek's pricing page. Second, a self-hosted DeepSeek deployment is not free. The API prices are the cheap path; running V4-Pro yourself in full precision requires enterprise GPU clusters, and even V4-Flash needs INT4 or INT8 quantization to fit on a single high-end consumer card. The open weights buy you control and remove per-token billing, but they shift cost into hardware and operations. For most teams the hosted DeepSeek API is the relevant comparison, and there Sol simply costs one to two orders of magnitude more per output token.

Benchmarks Compared

Benchmarks across two different labs are a minefield, because vendors pick favorable evaluations and report them their own way. We discipline this by leaning on the one independent evaluator that scores both models the same way — Artificial Analysis — and by treating vendor-reported figures as attributed claims, not verified facts. That distinction matters more than usual in this matchup, because the two models have very different amounts of independent data available.

The cleanest signal is the Artificial Analysis Intelligence Index, because it is one evaluator running the same battery on both: GPT-5.6 Sol at 59 versus V4-Pro at 44, a clear 15-point lead. The second independent signal points the same way and is where the gap is widest — the AA Coding Agent Index ranks Sol first at 80, while DeepSeek V4 is not placed on that leaderboard at all. Together those two independent, same-evaluator numbers are the backbone of the capability case for Sol, and they are consistent with each other.

Where we will not overreach is SWE-bench Verified. Sol is too new to be charted on the independent SWE-bench Verified leaderboard as of mid-July 2026, and DeepSeek's widely quoted 80.6 percent is a self-reported figure run on DeepSeek's own harness, not an independently verified result. So there is no clean independent head-to-head on that specific benchmark, and we do not manufacture one. The same honesty applies to Sol's Terminal-Bench 2.1 at 88.8 percent and SWE-bench Pro at 64.6 percent: those are OpenAI's own reported numbers, DeepSeek does not report the same way, and OpenAI itself questions the validity of SWE-bench Pro, so we present them as attributed claims with no DeepSeek counterpart rather than as a scoreboard. The numbers we can trust — the two Artificial Analysis indices — say clearly that GPT-5.6 Sol is the stronger model on measured capability, and that DeepSeek V4 is far closer on quality than its price would suggest.

Architecture and What Is Actually Different

It is tempting to treat two frontier models as interchangeable black boxes that you poke through an API, but the engineering underneath shapes how they behave, what they cost to run, and where they can be deployed. The two could hardly be more different in philosophy.

GPT-5.6 Sol is a closed model, so OpenAI discloses behavior rather than internals. What it surfaces is a product-level capability set built for agentic work: the full tool stack on by default, a reasoning-effort scale that now runs low, medium, high, xhigh, plus a new max level and an ultra multi-agent mode that spins up several reasoning agents in parallel, and a programmatic tool-calling feature that lets the model write and execute JavaScript in an isolated, ephemeral runtime. Snapshot pinning gives production teams reproducibility, prompt caching reads at a 90 percent discount, and the model is more token-efficient than its predecessor, biasing toward shorter responses that soften the per-task impact of the high rate card. The trade-offs are real and worth naming: there is no fine-tuning of the Sol base model, it is text and image in but text only out, and it cannot be moved off OpenAI's infrastructure at all.

DeepSeek V4 is the opposite — transparent at the architecture level because the weights and a technical report ship publicly. It is a mixture-of-experts model: V4-Pro carries 1.6 trillion total parameters with about 49 billion active per token, V4-Flash carries 284 billion total with about 13 billion active. The headline innovation is a Hybrid Attention design that combines Compressed Sparse Attention, at four-times compression, with Heavily Compressed Attention, at 128-times compression, to make a 1,000,000-token context affordable to serve. DeepSeek reports this cuts inference compute to a small fraction of the previous generation and shrinks the KV cache dramatically. It bakes three reasoning modes directly into the model rather than bolting them on as a separate API, and it is the first major Chinese frontier model with day-one inference on Huawei Ascend hardware, removing the hard dependency on a single chip vendor. This is why DeepSeek V4 can be both frontier-adjacent in quality and one to two orders of magnitude cheaper on output: the efficiency is engineered in, not just priced in.

The practical upshot is that GPT-5.6 Sol gives you a polished, deeply integrated, multimodal-input agent you cannot inspect or move, while DeepSeek V4 gives you an inspectable, movable, text-only model that you operate yourself. Neither philosophy is wrong; they serve different risk, cost, and sovereignty profiles.

Total Cost of Ownership

Per-token price is the headline, but the real economics depend on volume, caching, and whether you self-host. Here is how to think about it without overstating the case in either direction.

For the hosted-API path, the gap is so large that for high-volume workloads it changes what is buildable. A pipeline that processes, say, a billion output tokens a month costs about 30,000 dollars on GPT-5.6 Sol at standard pricing, around 15,000 dollars with the Batch API discount, roughly 870 dollars on DeepSeek V4-Pro, and about 280 dollars on V4-Flash. Those are not small percentage differences; they are different orders of magnitude, and they decide whether an idea is economically viable at all. Prompt caching narrows the input side meaningfully — Sol cache reads at 0.50 dollars per million tokens are genuinely cheap, and DeepSeek's cache hits at 0.003625 dollars for V4-Pro are almost free — but output dominates agentic spend, and there Sol has no answer to DeepSeek's pricing.

For the self-hosted path, the calculus flips from per-token billing to capital and operations. DeepSeek's open weights remove the API meter entirely, but you pay in hardware: full-precision V4-Pro requires enterprise GPU clusters, and even V4-Flash needs INT4 or INT8 quantization to fit a single high-end consumer card. For a team with steady, predictable, very high volume and the operational maturity to run model infrastructure, self-hosting V4 can be the cheapest option of all, and the only one that guarantees data never leaves your premises. For a team with spiky or modest volume, the hosted DeepSeek API is the sensible comparison — and it is still one to two orders of magnitude cheaper on output than Sol. The honest conclusion is that DeepSeek wins on cost in every scenario; the only questions are by how much and at what operational price. What you buy for Sol's premium is the measured capability lead and the compliance story, not cheaper tokens.

How We Tested

Honesty about methodology matters more in a cross-lab, cross-country comparison than almost anywhere else. Here is exactly what is hands-on and what is research.

We ran both models through their hosted APIs on coding and reasoning prompts to confirm they behave as documented — Sol's tool stack, its reasoning-effort scale including the new max and ultra modes, and its snapshot pinning, and DeepSeek V4's three thinking modes and OpenAI-compatible endpoint. Those behavioral observations are first-hand. What we have not done is stand up a self-hosted V4-Pro cluster, or run weeks of controlled, identical-task benchmarking of both models against each other on a private suite. For that reason, every capability claim that rests on a number is attributed to its source — Artificial Analysis for the independent Intelligence and Coding Agent indices, and OpenAI or DeepSeek for their own self-reported figures, each labeled as such. We pulled all pricing by fetching each vendor's pricing page directly rather than trusting secondhand summaries. Where we could not verify a like-for-like number — most importantly on SWE-bench Verified, where Sol is not yet charted and DeepSeek's figure is self-reported — we said so and left the head-to-head uncommitted. That is the standard we hold ourselves to, and it is the only honest way to compare a closed US model against an open Chinese one.

Winner by Category

A single overall winner would be dishonest here, because these models are tuned for different buyers. Here is who wins what.

• Best for peak measured intelligence: GPT-5.6 Sol. It sits at 59 on the Artificial Analysis Intelligence Index, a clear 15 points ahead of V4-Pro at 44.
• Best for measured coding: GPT-5.6 Sol. It ranks first on the AA Coding Agent Index at 80, a charted independent score DeepSeek V4 does not have, and it ships the full agentic tool stack — function calling, web search, code interpreter, computer use, and MCP — on by default.
• Best for cost: DeepSeek V4. Roughly 34 times cheaper per output token on V4-Pro and over 100 times cheaper on V4-Flash, with cache-hit input pricing that is close to free.
• Best for open weights and self-hosting: DeepSeek V4. MIT-licensed downloadable weights, with native Huawei Ascend support; Sol cannot be self-hosted at all.
• Best for Western data residency and compliance: GPT-5.6 Sol. US-hosted with regional residency endpoints; DeepSeek's hosted API runs in China, and self-hosting is the only compliant path to the open weights for many buyers.
• Best for long-context work: Near-tie, edge to Sol on raw size (1,050,000 versus 1,000,000 tokens), though DeepSeek allows up to 384K output tokens against Sol's 128K, so heavy generation jobs can favor DeepSeek.
• Best for multimodal input: GPT-5.6 Sol. It accepts image input alongside text; DeepSeek V4 is text only, so any image-in-the-loop workflow needs a separate vision model.

Pros and Cons

When to Pick Each

When to pick GPT-5.6 Sol

Pick GPT-5.6 Sol when capability and integration matter more than per-token cost. If you are doing serious agentic coding, computer-use automation, or multi-step task decomposition on the OpenAI stack, it is the stronger model on both independent indices, and its tool stack, image input, and reasoning-effort control — including the new max and ultra modes — give you fine-grained leverage DeepSeek does not match out of the box. Pick it if you are a Western enterprise with data-residency or compliance obligations, because US hosting and regional residency endpoints clear bars DeepSeek's China-hosted API cannot. And pick it if your workload is moderate in volume but high in value, where paying 30 dollars per million output tokens for the best result is a rounding error against engineer time. If you live inside ChatGPT, Codex, or the Responses API, Sol is the natural default.

When to pick DeepSeek V4

Pick DeepSeek V4 when cost, control, or sovereignty dominate. If you are running high-volume inference where token spend is the binding constraint, a one-to-two-orders-of-magnitude cheaper API changes what is economically viable — and V4-Flash at 0.28 dollars output makes use cases that are simply unaffordable on Sol routine. Pick it if you need to own your weights: the MIT license lets you self-host, fine-tune, and redistribute, and the Huawei Ascend support means you are not locked to a single chip vendor. Pick it if you are operating where Chinese hosting is acceptable, or where self-hosting is mandatory for data sovereignty, or where you simply need the largest possible output generation at 384K tokens. You give up a measurable slice of frontier capability, the charted coding score, multimodal input, and the Western compliance story, but you get most of the quality at a tiny fraction of the price.

Final Verdict

This is a split verdict by use case, tilted toward GPT-5.6 Sol on capability and toward DeepSeek V4 on cost and openness. On the two independent signals that score both — the Artificial Analysis Intelligence Index and the AA Coding Agent Index — Sol leads 59 to 44 on intelligence and ranks first at 80 on coding where DeepSeek V4 is not charted at all. It is the stronger model, the more deeply integrated one for agentic work, the only one that reads images, and the only one that clears Western data-residency requirements. DeepSeek V4, in return, costs roughly 34 times less per output token on V4-Pro and over 100 times less on V4-Flash, ships MIT-licensed open weights you can self-host anywhere, matches Sol on context, and beats it on maximum output length — a genuinely remarkable package for an open model.

We did not crown a single overall winner because the two models are not really competing for the same buyer. If you need the strongest measured model, the top charted coding score, multimodal input, or US-hosted compliance, the answer is GPT-5.6 Sol. If you are cost-constrained, want to own your weights, or need to self-host for sovereignty, the answer is DeepSeek V4. Both answers are correct — for different people. Every benchmark number here is either drawn from the Artificial Analysis independent indices or explicitly attributed to a vendor's own report; only the pricing is fetch-verified directly from each vendor.

If you are weighing Sol against other cost-efficient models, we also ran DeepSeek V4 head-to-head with Sol's predecessor in GPT-5.5 vs DeepSeek V4, and against Anthropic's mid-tier flagship in Claude Sonnet 5 vs DeepSeek V4. For the deep dive on each model on its own, see our full GPT-5.6 Sol review and DeepSeek V4 review.

Frequently Asked Questions

Is GPT-5.6 Sol better than DeepSeek V4?

On measured capability, yes. GPT-5.6 Sol tops the Artificial Analysis Intelligence Index at 59 versus 44 for DeepSeek V4-Pro, and it ranks first on the AA Coding Agent Index at 80, a charted independent score DeepSeek V4 does not have. But DeepSeek V4 is roughly 34 times cheaper per output token on V4-Pro and is open-weight and self-hostable, so the better choice depends on whether you are optimizing for capability and compliance or for cost and control.

How much cheaper is DeepSeek V4 than GPT-5.6 Sol?

Dramatically. On output tokens, DeepSeek V4-Pro at 0.87 dollars per million is roughly 34 times cheaper than GPT-5.6 Sol at 30 dollars per million, and V4-Flash at 0.28 dollars is over 100 times cheaper. On input tokens, V4-Pro at 0.435 dollars is about 11 times cheaper than Sol at 5 dollars, and V4-Flash at 0.14 dollars is about 36 times cheaper. All prices were fetched directly from each vendor's pricing page in July 2026.

Is DeepSeek V4 open source?

It is open weights, not fully open source. DeepSeek V4 ships its model weights under an MIT license on Hugging Face, allowing free commercial use, redistribution, and modification. However, the training code and data recipe are not released, so the community cannot fully reproduce the training run. You can self-host and fine-tune the model, but you cannot rebuild it from scratch. GPT-5.6 Sol, by contrast, is fully closed and cannot be self-hosted at all.

Can I self-host DeepSeek V4 or GPT-5.6 Sol?

You can self-host DeepSeek V4 because its weights are MIT-licensed and downloadable, including native support for Huawei Ascend chips. You cannot self-host GPT-5.6 Sol — it is a closed model available only through OpenAI's API, ChatGPT, and Codex. Self-hosting V4 requires serious hardware: full-precision V4-Pro needs enterprise GPU clusters, and V4-Flash needs quantization to fit a single high-end consumer card.

What is the context window for each model?

GPT-5.6 Sol ships a 1,050,000-token context window with up to 128K output tokens. DeepSeek V4 provides 1,000,000 tokens of context on both V4-Pro and V4-Flash, with up to 384K tokens of output. The two are effectively tied on raw context length, with Sol slightly larger on input and DeepSeek larger on maximum output — so heavy generation jobs can actually favor DeepSeek.

Which model is better for coding?

GPT-5.6 Sol leads on the one independent coding signal that charts it: the Artificial Analysis Coding Agent Index, where it ranks first at 80 and DeepSeek V4 is not placed at all. Sol also ships a full agentic tool stack on by default. DeepSeek self-reports 80.6 percent on SWE-bench Verified, but that is a vendor figure on its own harness, not an independently charted result, so we do not treat it as a head-to-head. DeepSeek V4 is still strong and far cheaper, which makes it attractive for high-volume coding where cost dominates over the last few points of measured capability.

Is DeepSeek V4 safe to use for a Western company?

It depends on your data-residency rules. DeepSeek's hosted API runs in China, which keeps many regulated buyers — US Federal, EU healthcare — from adopting it without a Western reseller. The MIT-licensed open weights let you sidestep this by self-hosting the model on your own infrastructure anywhere in the world. If compliance is the concern and you cannot self-host, GPT-5.6 Sol's US hosting and regional residency endpoints are the safer default.

How do the two models score on independent benchmarks?

The cleanest independent signals come from Artificial Analysis, which scores both with the same battery. On the Intelligence Index, GPT-5.6 Sol sits at 59 while DeepSeek V4-Pro in maximum reasoning mode scores 44. On the AA Coding Agent Index, Sol ranks first at 80 while DeepSeek V4 is not on the leaderboard. Sol is not yet charted on the independent SWE-bench Verified leaderboard, and DeepSeek's 80.6 percent on that benchmark is self-reported, so we do not present a SWE-bench head-to-head.

What are the different DeepSeek V4 tiers?

DeepSeek V4 ships in two sizes. V4-Pro is a 1.6-trillion-parameter mixture-of-experts model with about 49 billion parameters active per token, priced at 0.435 dollars input and 0.87 dollars output per million tokens. V4-Flash is a 284-billion-parameter model with about 13 billion active, priced at 0.14 dollars input and 0.28 dollars output. Both carry a 1,000,000-token context window with up to 384K output, and both support three reasoning modes — Non-Think, Think High, and Think Max.

Does GPT-5.6 Sol have a cheaper mode?

Yes, two cost levers. The Batch API offers a 50 percent discount for asynchronous workloads, bringing GPT-5.6 Sol to 2.50 dollars input and 15 dollars output per million tokens. Prompt caching drops repeated input to 0.50 dollars per million tokens on cache reads. Even with both applied, however, Sol remains an order of magnitude more expensive per output token than DeepSeek V4's hosted API. If cost is the binding constraint, OpenAI's cheaper tiers Terra and Luna are more relevant than pushing Sol.

Which should I choose for a high-volume production workload?

For pure high volume where token cost is the binding constraint, DeepSeek V4 is usually the rational choice — a one-to-two-orders-of-magnitude cheaper API makes workloads viable that GPT-5.6 Sol cannot support economically. Choose Sol instead when each call is high-value, when you need the strongest measured coding and tool integration, when you need image input, or when Western data residency is mandatory. Many teams run both: Sol for the hardest tasks, DeepSeek V4 for everything bulk.

When were these models released and is this comparison current?

GPT-5.6 Sol became generally available July 9, 2026, across ChatGPT, Codex, and the API. DeepSeek V4 shipped April 24, 2026. This comparison was last updated in July 2026, with all pricing fetched directly from each vendor's pricing page at that time and all benchmark figures either drawn from the Artificial Analysis independent indices or attributed to each vendor's own reports.