GPT-5.6 Sol vs Kimi K2.7: Premium Flagship vs Open-Weight Challenger (2026)

GPT-5.6 Sol and Kimi K2.7 are opposite bets in the 2026 model market: Sol is OpenAI's premium closed flagship, Kimi K2.7 is Moonshot AI's open-weight challenger. On the independent Artificial Analysis Intelligence Index v4.1, Sol scores 59 to Kimi's 42, adds the No.1 Coding Agent Index at 80 (Kimi is not listed there), runs a 1,050,000-token context to Kimi's 256K, and outputs faster at about 74.5 tokens per second versus 47.6. Kimi answers on price and freedom: $0.95 input and $4.00 output per million tokens against Sol's $5.00 and $30.00 — roughly five to seven and a half times cheaper — plus open weights under a Modified MIT license you can self-host today. Best for peak capability and the largest context: GPT-5.6 Sol. Best for lowest cost and open-weight control: Kimi K2.7. There is no single overall winner — you pick the priority.

Quick Verdict

This is a split verdict between two different philosophies: GPT-5.6 Sol owns measured capability, context, and ecosystem, while Kimi K2.7 owns price and openness — and because the two barely overlap, the choice is unusually clear. GPT-5.6 Sol reached general availability on July 9, 2026, and we ran it directly through our own OpenAI API key. Kimi K2.7 was announced by Moonshot AI on June 12, 2026, and we assessed it against Moonshot's documentation, its open-weight release, our own hands-on Kimi K2.7 review, and the independent Artificial Analysis measurements. Every figure below carries its source, and each vendor's self-reported numbers are labeled as such, per OpenAI's own announcement. Here is the short version.

• Best for peak measured intelligence: GPT-5.6 Sol. Artificial Analysis scores it 59 on the Intelligence Index v4.1 against Kimi's 42 — a seventeen-point gap on the same independent harness.
• Best on the agentic coding index: GPT-5.6 Sol. It ranks No.1 at 80 on the AA Coding Agent Index; Kimi K2.7 is not listed on that index, so there is no independent agentic-coding score to set against it.
• Best for context size: GPT-5.6 Sol. Its 1,050,000-token window is roughly four times Kimi's 262,144-token (256K) context.
• Best for output speed: GPT-5.6 Sol. Artificial Analysis clocks it at about 74.5 tokens per second against Kimi's 47.6.
• Best for price: Kimi K2.7, decisively. At $0.95 input and $4.00 output per million tokens it is roughly five times cheaper on input and about seven and a half times cheaper on output than Sol. Both rate cards are vendor-verified.
• Best for open weights and self-hosting: Kimi K2.7. It ships downloadable weights under a Modified MIT license on HuggingFace; Sol is a closed API with no self-host path.
• Best for native vision in coding: Kimi K2.7. Its built-in 400-million-parameter MoonViT encoder reads screenshots and diagrams, and Moonshot documents image and video input; Sol takes image input but has no native vision encoder of that kind.
• No single overall winner: Sol wins capability, context, and speed; Kimi wins cost, openness, and multimodal breadth. The right pick is whichever axis is binding for you.

The honest caveats up front: GPT-5.6 Sol is only days old at the time of writing, so we treat our hands-on notes as first impressions rather than a settled verdict, and we did not run Kimi K2.7 through a controlled head-to-head — our Kimi assessment leans on our published review, Moonshot's documentation, and the independent Artificial Analysis index. Neither model has an independent SWE-bench Verified score: OpenAI has not submitted Sol, and Moonshot skipped the standard public suites for Kimi, so each headline coding figure below that is not from Artificial Analysis is self-reported in the vendor's own harness, and we label it that way.

GPT-5.6 Sol vs Kimi K2.7 — 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. 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 carries Programmatic Tool Calling, where the model writes and executes JavaScript in an isolated, ephemeral runtime to orchestrate its own tools, plus a deep native tool stack. API pricing is $5 per million input tokens and $30 per million output tokens, with cached input at $0.50 per million. On the independent leaderboards it is a frontier model: 59 on the Artificial Analysis Intelligence Index and No.1 at 80 on the Coding Agent Index. It is a closed model — there are no downloadable weights. Our full GPT-5.6 Sol review covers the hands-on detail.

What Is Kimi K2.7?

Kimi K2.7 (also called Kimi K2.7-Code) is Moonshot AI's open-weight coding model, announced June 12, 2026. It is a 1-trillion-parameter Mixture-of-Experts design with 32 billion active parameters — 384 experts with 8 selected per token — and a 262,144-token (256K) context window, released under a Modified MIT license with weights published on HuggingFace that anyone can download and self-host. Per Moonshot's Kimi platform pricing documentation, the metered API costs $0.95 per million input tokens on a cache miss, $0.19 per million on a cache hit, and $4.00 per million output tokens, with automatic context caching for cheaper repeated long-context calls. It includes a native 400-million-parameter MoonViT vision encoder for reading screenshots and diagrams, exposes an OpenAI-compatible API with tool calls and JSON mode, and — per Moonshot — uses roughly 30 percent fewer reasoning tokens than its predecessor Kimi K2.6 to reach a higher score. On the independent Artificial Analysis Intelligence Index it scores 42, a leading result among open-weight models but seventeen points behind Sol. Moonshot did not submit Kimi to the standard public benchmark suites, so its other headline numbers are self-reported. Our full Kimi K2.7 review has the architecture detail.

How We Compared Them — and What We Did Not Do

Method transparency matters here, because one model is days old and the other we assessed rather than ran head-to-head. Here is exactly what we did and did not do, and where every number comes from.

• Pricing: both rate cards are vendor-verified. We confirmed Sol's $5 input and $30 output per million tokens directly on OpenAI's API pricing documentation, and Kimi's $0.95 input, $0.19 cached, and $4.00 output on Moonshot's Kimi platform pricing page. No relayed figures.
• Independent benchmarks: we lean on Artificial Analysis for the Intelligence Index, the Coding Agent Index, and output speed, because it is the one harness that measures both models on the same basis. Where a model has not been measured — Kimi on the Coding Agent Index, and both models on SWE-bench Verified — we say so and do not substitute a self-reported number.
• Self-reported figures: OpenAI's Terminal-Bench 2.1 number for Sol (88.8 percent) and Moonshot's Kimi Code Bench v2 (62.0) and MCP Mark Verified (81.1) numbers for Kimi are labeled as vendor-reported throughout, measured in different in-house harnesses, and are not treated as head-to-head evidence.
• Hands-on: we ran GPT-5.6 Sol through our own OpenAI API key on coding and reasoning tasks within days of its July 9 general availability — sharp first impressions, not a controlled benchmark. We did not run a controlled head-to-head of Kimi K2.7; our Kimi assessment draws on our published review, Moonshot's documentation, its open-weight release, and the independent Artificial Analysis scores.
• Disclosure: we have no affiliate relationship with OpenAI or Moonshot AI. There are no sponsored links on this page. This comparison spans a closed US flagship and an open Chinese challenger, and we have no incentive to favor either — only the question of which fits which job.

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, self-reported figures, and genuine ties. Sources for the independent scores are Artificial Analysis, and the specifications come from OpenAI's model documentation and Moonshot's Kimi platform documentation.

Synthesis: read top to bottom, the table splits cleanly down the middle. The price and openness rows all go to Kimi K2.7, and they go decisively: five to seven and a half times cheaper per token, plus downloadable weights that Sol structurally cannot offer. The capability rows all go to GPT-5.6 Sol, and by wide margins on the one independent yardstick they share: seventeen points on the Intelligence Index, the No.1 Coding Agent Index where Kimi is absent, a four-times-larger context, and faster output. Two rows are honest ties — neither model is on the independent SWE-bench Verified leaderboard, and each vendor's self-reported coding number lives in its own harness, so they cannot be set against each other. One nuance worth stating plainly: on LMArena's human-preference Elo, Sol's Xhigh configuration is charted at 1486 (No.8), while Kimi K2.7 has not been charted there, so we do not treat human preference as a head-to-head row — only Sol has a number. That leaves the decision exactly where the two design philosophies put it: capability against cost and control.

Pricing — GPT-5.6 Sol vs Kimi K2.7 in 2026

Pricing is the sharpest divide in this comparison. Kimi K2.7 is not a little cheaper than GPT-5.6 Sol — it is multiples cheaper on every line, and then it removes the API bill entirely for anyone willing to self-host the open weights. For the mechanics of input, output, and cached-token billing, our AI model pricing explainer breaks down how these rate cards translate into real invoices. Both tables below come straight from the vendors' own documentation: Sol from OpenAI's API pricing, Kimi from Moonshot's Kimi platform pricing.

GPT-5.6 Sol Pricing

Kimi K2.7 Pricing

Pricing verdict: Kimi K2.7 wins price, and it is not close. 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 Kimi costs about $0.065 ($0.95 times 0.05 plus $4 times 0.005) — roughly six times less for that same call. The gap widens on output-heavy work, where Kimi's $4 per million sits seven and a half times below Sol's $30, and it widens again for teams that self-host the open weights and pay only for compute. What Sol's premium buys is not a cheaper invoice but a higher ceiling: seventeen more points of measured intelligence, the No.1 coding index, four times the context, and a managed frontier service. If cost per token is your binding constraint, Kimi is the rational choice; Sol earns its price only where its capability lead actually changes outcomes.

Hands-On and Assessment Notes

We owe you precision about what this section is and is not. We ran GPT-5.6 Sol directly through our own OpenAI API key within days of its July 9 general availability, which gives us sharp first impressions but nowhere near a controlled benchmark. We did not run Kimi K2.7 in a matched head-to-head; our Kimi observations come from our published Kimi K2.7 review, Moonshot's documentation, and the independent Artificial Analysis measurements. Weight the attributed numbers and OpenAI's model documentation above our short hands-on window.

Where Sol stood out in our runs: the hardest single problems. On a deliberately tricky algorithm task, Sol wrote a correct implementation on the first try and reasoned cleanly through a multi-step logic puzzle; on a source-comprehension prompt it correctly refused to invent a fact the text withheld rather than guessing. Turned up to its higher reasoning levels, and especially in the ultra multi-agent mode, it produced visibly more thorough plans on a hard architecture task — at a higher token bill for that call. This lines up with its 59 Intelligence Index and No.1 Coding Agent Index placement without proving either in a few days.

What the record shows for Kimi: a strong, affordable open-weight coder with a thinner independent paper trail. Its 42 on the Artificial Analysis Intelligence Index is a leading open-weight result, and its native MoonViT vision encoder is a genuine, unusual strength for reading screenshots inside coding tasks. But Moonshot skipped the standard public benchmark suites, so beyond Artificial Analysis its headline numbers — 62.0 on Kimi Code Bench v2, 81.1 on MCP Mark Verified — are self-reported in its own harness and cannot be set directly against Sol's. Its clearest advantages are structural rather than benchmark-driven: the price, the open weights, and the freedom to self-host.

What the split looked like in practice: the honest pattern is that these two models rarely compete for the same task. Sol is the choice when capability, context, or a verifiable agentic-coding score decides the outcome; Kimi is the choice when cost, data control, or self-hosting decides it. That is exactly why we did not crown one winner — the models answer different questions.

What we cannot tell you yet: a matched, controlled head-to-head on identical tasks, Kimi's real-world coding quality against Sol at scale, and how either model's early behavior holds up over weeks of production use. We will update this comparison as our side-by-side time accumulates and as more independent harnesses publish results for both.

Winner per Category

Best for Peak Measured Intelligence: GPT-5.6 Sol

On the Artificial Analysis Intelligence Index v4.1, GPT-5.6 Sol scores 59 against Kimi K2.7's 42 — a seventeen-point gap on the same independent harness, and the widest capability separation in this comparison. That is not a rounding difference: on the hardest reasoning tasks, a gap of that size regularly decides whether a multi-step answer is correct or merely plausible. Kimi's 42 is a genuinely strong open-weight result, and for many everyday prompts the two will feel closer than the numbers suggest. But if your workload leans on the toughest reasoning you have, Sol is the clear pick on the independent evidence, and our GPT-5.6 Sol review covers where that headroom shows up.

Best on the Agentic Coding Index: GPT-5.6 Sol

The AA Coding Agent Index is the sharpest capability separator here, and it is one-sided by absence: Sol ranks No.1 at 80, while Kimi K2.7 is not listed on that index at all, so there is no independent agentic-coding score to place beside Sol's. Neither model has an independent SWE-bench Verified result either — OpenAI has not submitted Sol, and Moonshot skipped the public suites for Kimi — so the Coding Agent Index is the best like-for-like agentic-coding signal available, and only Sol appears on it. Our explainer on agentic coding models covers why an agent index measures something different from a single-shot coding score. Kimi remains a capable, cheap coding model in practice; it simply lacks the independent agentic-coding record Sol has.

Best for Context and Reasoning Depth: GPT-5.6 Sol

Sol owns the envelope. Per OpenAI's documentation, it runs a 1,050,000-token context — roughly four times Kimi's 262,144-token window — and its reasoning-effort scale climbs through xhigh and max to ultra, a multi-agent mode that runs up to sixteen reasoning agents in parallel. Kimi K2.7 has no equivalent multi-agent tier; it exposes tool calls and JSON mode through an OpenAI-compatible API. For whole-repository prompts, very long documents, or long-horizon autonomous agents that benefit from parallel reasoning, Sol's larger context and higher reasoning ceiling are concrete, Kimi-unavailable advantages. Kimi's automatic context caching lowers the cost of using its 256K window, but it cannot extend the window itself.

Best for Price and Cost per Token: Kimi K2.7

This one is not close, and it runs the other way. Kimi K2.7 costs $0.95 per million input tokens against Sol's $5, and $4 per million output against $30 — roughly five times cheaper on input and seven and a half times cheaper on output, both vendor-verified on Moonshot's pricing page and OpenAI's. Cached input is $0.19 per million on Kimi against $0.50 on Sol. And because Kimi is open-weight, a team running it on its own GPUs can push the marginal cost down to compute alone. Unless your tasks demonstrably need Sol's capability lead, Kimi delivers many times the output per dollar — the single largest advantage in this matchup, and the reason it exists.

Best for Open Weights and Self-Hosting: Kimi K2.7

Openness is a category Sol cannot enter. Kimi K2.7 ships downloadable weights under a Modified MIT license on HuggingFace, so you can run the full 1-trillion-parameter Mixture-of-Experts model on your own hardware, keep data on-premises, and avoid any single-vendor dependency. GPT-5.6 Sol is a closed model reached only through OpenAI's API, ChatGPT, and Codex — there are no weights to download and no self-host path, per OpenAI's announcement. For teams with data-residency requirements, air-gapped environments, or a hard rule against vendor lock-in, Kimi is not merely cheaper — it is the only one of the two that can meet the requirement at all. Our guide to closed versus open-weight models walks through when that matters most.

Best for Native Vision in Coding: Kimi K2.7

One narrow but real edge for Kimi sits in multimodal input. It includes a native 400-million-parameter MoonViT vision encoder built to read screenshots, UI mockups, and diagrams inside coding workflows, and Moonshot's platform documentation lists text, image, and video input for the model. GPT-5.6 Sol accepts text and image input too, but it has no comparable native vision encoder and no video intake, and its image generation is a callable tool rather than an input or output modality. For most text-heavy coding the difference is marginal, but for agents that reason over UI mockups, error screenshots, or design diagrams, Kimi's built-in vision is a genuine, practical advantage over Sol.

Pros and Cons

GPT-5.6 Sol Pros and Cons

What we like about GPT-5.6 Sol

• Highest measured capability by a wide margin. 59 on the Artificial Analysis Intelligence Index against Kimi's 42, and No.1 at 80 on the Coding Agent Index where Kimi is not listed.
• Largest context window. 1,050,000 tokens, roughly four times Kimi's 256K, for whole-repository and long-document work.
• Exclusive ultra multi-agent reasoning mode. Up to sixteen parallel reasoning agents for the hardest long-horizon problems — a ceiling Kimi does not offer.
• Faster output. About 74.5 tokens per second on Artificial Analysis against Kimi's 47.6.
• Disciplined hands-on behavior. In our runs it wrote a correct hard algorithm on the first try and refused to hallucinate a withheld fact rather than guessing.

Where GPT-5.6 Sol falls short

• Far more expensive on every line. $5 input and $30 output per million tokens — roughly five to seven and a half times Kimi's rates.
• Closed model, no self-hosting. API-only, with no downloadable weights, so it cannot meet on-premises or data-residency requirements.
• Absent from independent SWE-bench Verified. Not submitted, so it has no independent verified-coding number — the same gap as Kimi.
• Headline coding figures beyond the AA indices are self-reported. Terminal-Bench 2.1 comes from OpenAI, not an independent harness.
• Days old at the time of writing. Its production behavior over weeks is unproven, so our hands-on notes are first impressions.

Kimi K2.7 Pros and Cons

What we like about Kimi K2.7

• Radically cheaper per token. $0.95 input and $4 output per million tokens, roughly five to seven and a half times below Sol, with cached input at $0.19.
• Open weights under a Modified MIT license. Downloadable on HuggingFace and self-hostable today — no vendor lock-in, on-premises friendly.
• Leading open-weight intelligence score. 42 on the independent Artificial Analysis Intelligence Index, strong for a downloadable model.
• Native MoonViT vision encoder. A built-in 400-million-parameter encoder for reading screenshots and diagrams, with documented image and video input.
• Efficient MoE design and OpenAI-compatible API. 32 billion active parameters of a 1-trillion-parameter model, about 30 percent fewer reasoning tokens than K2.6, and drop-in API compatibility.

Where Kimi K2.7 falls short

• Seventeen points behind Sol on measured intelligence. 42 to 59 on the same Artificial Analysis harness.
• No independent agentic-coding score. Not listed on the AA Coding Agent Index, so its agentic-coding capability has no independent yardstick against Sol's.
• Smaller context window. 262,144 tokens (256K) against Sol's 1,050,000, which matters for whole-repository prompts.
• Every non-AA benchmark is self-reported. Moonshot skipped SWE-bench Verified and the other public suites, so numbers like 62.0 on Kimi Code Bench v2 come from its own harness.
• Slower output and no multi-agent tier. 47.6 tokens per second on Artificial Analysis, and no equivalent to Sol's ultra reasoning mode.

When to Pick GPT-5.6 Sol vs Kimi K2.7

Pick GPT-5.6 Sol if...

• Your workload is the hardest coding, long-horizon agents, science, or computer use, where the seventeen-point intelligence gap and No.1 coding index actually change outcomes.
• You need a context window beyond 256K — whole-repository prompts, very long documents, or extended autonomous sessions.
• You want the ultra multi-agent reasoning mode (up to sixteen parallel agents) that Kimi does not offer.
• You value an independent, verifiable capability record and a deep native tool stack over the lowest possible token price.
• A managed, closed frontier API fits your compliance posture and you do not need self-hosting.

Pick Kimi K2.7 if...

• Cost per token is the deciding factor — roughly five to seven and a half times cheaper than Sol on the metered API, and compute-only if you self-host.
• You need open weights: on-premises deployment, data residency, air-gapped environments, or freedom from single-vendor lock-in.
• Your coding agents read screenshots, UI mockups, or diagrams and benefit from a native vision encoder.
• You run high-volume, cost-sensitive coding where a leading open-weight model is good enough and the price advantage compounds on every call.
• You want to self-host the bulk of traffic and reserve a paid frontier API only for the tasks that measurably need one.

Frequently Asked Questions

Is GPT-5.6 Sol better than Kimi K2.7 in 2026?

On raw measured capability, yes, and by a clear margin — but they are built for different jobs, so there is no single winner. On the independent Artificial Analysis Intelligence Index v4.1, GPT-5.6 Sol scores 59 against Kimi K2.7's 42, a seventeen-point gap on the same harness, and Sol is No.1 at 80 on the Coding Agent Index while Kimi is not listed there. Sol also runs a larger 1,050,000-token context and faster output. But Kimi K2.7 costs a fraction as much — $0.95 input and $4.00 output per million tokens versus Sol's $5.00 and $30.00 — and ships open weights you can download and self-host under a Modified MIT license, which Sol's closed API cannot do. For the hardest work and peak capability, Sol is the stronger model; for cost, control, and self-hosting, Kimi is the better fit. It depends on your priority, not on one being universally better.

How much do GPT-5.6 Sol and Kimi K2.7 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; we confirmed those rates directly on OpenAI's API pricing documentation. Kimi K2.7 costs $0.95 per million input tokens on a cache miss, $0.19 per million on a cache hit, and $4.00 per million output tokens, which we verified on Moonshot's own Kimi platform pricing page. That makes Kimi roughly five times cheaper on input and about seven and a half times cheaper on output. Kimi is also open-weight, so beyond the metered API you can run it on your own hardware and pay only for compute. Sol has no self-host option — it is a closed API only. On any given prompt, Kimi bills a small fraction of what Sol bills.

What is the difference between GPT-5.6 Sol and Kimi K2.7?

They are opposite ends of the 2026 model market. GPT-5.6 Sol is OpenAI's flagship capability tier of the GPT-5.6 generation, a closed model reached only through OpenAI's API, ChatGPT, and Codex, tuned for the hardest coding, long-horizon agents, and research. Kimi K2.7 is Moonshot AI's open-weight coding model — a 1-trillion-parameter Mixture-of-Experts design with 32 billion active parameters, released under a Modified MIT license with weights on HuggingFace that anyone can download and self-host. Sol leads on independent capability benchmarks and context size; Kimi leads on price and openness. Sol offers a deep native tool stack and an ultra multi-agent reasoning mode; Kimi offers an OpenAI-compatible API, a native MoonViT vision encoder, and freedom from vendor lock-in. One is the premium closed ceiling, the other the affordable open floor.

Which is cheaper, GPT-5.6 Sol or Kimi K2.7?

Kimi K2.7, decisively, on every line of the rate card. Kimi costs $0.95 per million input tokens against Sol's $5.00, and $4.00 per million output tokens against Sol's $30.00 — about five times cheaper on input and seven and a half times cheaper on output. Cached input is $0.19 per million on Kimi versus $0.50 on Sol. Both figures are vendor-verified: Kimi's on Moonshot's Kimi platform pricing page, Sol's on OpenAI's API pricing documentation. And because Kimi is open-weight, a team running it at scale on its own GPUs can drop the per-token cost further still, paying only for infrastructure. If cost per token is your binding constraint, this comparison is not close — Kimi wins price outright. What Sol's premium buys is capability, context, and ecosystem, not a better invoice.

Is Kimi K2.7 open source, and can I self-host it?

Kimi K2.7 is open-weight, and yes, you can self-host it. Moonshot AI released the model under a Modified MIT license with the weights published on HuggingFace, so you can download the 1-trillion-parameter Mixture-of-Experts checkpoint and run it on your own hardware today. The Modified MIT license adds an attribution clause for very large commercial deployments above a user threshold, which is irrelevant for most teams but means it is not pure MIT. GPT-5.6 Sol offers no equivalent — it is a closed model available only through OpenAI's API, ChatGPT, and Codex, with no downloadable weights and no self-hosting path. For organizations that need on-premises deployment, data residency, or freedom from a single vendor's API, Kimi's open weights are the decisive advantage; Sol simply cannot meet that requirement.

Which is better for coding: GPT-5.6 Sol or Kimi K2.7?

On the independent evidence available, GPT-5.6 Sol. Sol ranks No.1 at 80 on the Artificial Analysis Coding Agent Index, which measures agentic, multi-step coding on a common harness; Kimi K2.7 is not listed on that index, so there is no independent agentic-coding score to set against Sol's. Neither model has an independent SWE-bench Verified result — OpenAI has not submitted Sol, and Moonshot skipped the public suites for Kimi — so that data gap applies to both. Kimi's headline coding number, 62.0 on Kimi Code Bench v2, comes from Moonshot's own in-house harness and is not comparable to Sol's independent index. For the strongest verifiable agentic-coding signal, Sol leads; for cheap, high-volume coding where you self-host and control cost, Kimi remains a strong practical pick despite the thinner independent record.

How do GPT-5.6 Sol and Kimi K2.7 compare on independent benchmarks?

They share exactly one independent yardstick, Artificial Analysis, and on it Sol leads clearly. On the Intelligence Index v4.1, GPT-5.6 Sol scores 59 to Kimi K2.7's 42 — a seventeen-point gap measured on the same evaluation suite. On output speed, Artificial Analysis clocks Sol at about 74.5 tokens per second and Kimi at 47.6. Sol is also No.1 at 80 on the Coding Agent Index, where Kimi is not listed. Beyond Artificial Analysis, neither model appears on the independent SWE-bench Verified leaderboard. Everything else each vendor publishes is self-reported in its own harness — OpenAI's Terminal-Bench 2.1 figure of 88.8 percent for Sol, and Moonshot's 62.0 on Kimi Code Bench v2 and 81.1 on MCP Mark Verified for Kimi — and those numbers are not comparable across labs. We keep the independent Artificial Analysis scores separate from the self-reported ones throughout.

Does Kimi K2.7 have a bigger context window than GPT-5.6 Sol?

No — GPT-5.6 Sol's context window is much larger. Per OpenAI's model documentation, Sol runs a 1,050,000-token context, roughly four times the 262,144 tokens (256K) that Moonshot lists for Kimi K2.7. For whole-repository prompts, very long documents, or long autonomous agent sessions, Sol's window is a real advantage. Kimi's 256K is still generous by 2026 standards and is paired with automatic context caching that drops repeated long-context input to $0.19 per million tokens, which softens the cost of large prompts. But if your workflow genuinely needs to hold a million-plus tokens in a single call, Sol is the one that can do it and Kimi cannot. On context size, this is a clear win for Sol; on the cost of using long context, Kimi's caching narrows the practical gap.

Is Kimi K2.7 multimodal like GPT-5.6 Sol?

Both accept image input, and Kimi K2.7 actually goes a step further on paper. GPT-5.6 Sol takes text and image inputs and returns text; it has no native audio, and its image generation is a callable tool rather than an output modality. Kimi K2.7 includes a native 400-million-parameter MoonViT vision encoder for reading screenshots, UI mockups, and diagrams inside coding tasks, and Moonshot's platform documentation lists text, image, and video input for the model. So for reading visual material in a coding workflow, Kimi's built-in vision encoder is a genuine strength, and its documented video input exceeds Sol's image-only intake. Neither model generates images or audio as a native output. If your workflow feeds screenshots or diagrams into a coding agent, Kimi's native vision is a point in its favor; for most text-heavy work the difference is marginal.

Should I use GPT-5.6 Sol and Kimi K2.7 together in the same stack?

For many teams, yes, and a split stack is the rational setup. Both expose an OpenAI-compatible surface — Kimi is explicitly OpenAI-API-compatible, and Sol is the OpenAI API — so an abstraction layer such as the Vercel AI SDK, LangChain, or LiteLLM can route between them by configuration rather than a rewrite. A practical pattern sends the hardest reasoning, the largest-context prompts, and the most demanding agentic coding to GPT-5.6 Sol, and sends high-volume, cost-sensitive, or self-hosted work to Kimi K2.7 at a fraction of the token cost. Because Kimi is open-weight, you can even run the bulk of traffic on your own hardware and reserve Sol's paid API for the tasks that measurably need its capability lead. The two are complements as often as they are rivals: Sol for the ceiling, Kimi for the volume.

Why does GPT-5.6 Sol cost so much more than Kimi K2.7?

You are paying for measured capability, context size, ecosystem, and a closed managed service. On the independent Artificial Analysis Intelligence Index, Sol's 59 sits seventeen points above Kimi's 42, and Sol adds the No.1 Coding Agent Index, a 1,050,000-token context, faster output, a deep native tool stack, and an ultra multi-agent reasoning mode that runs up to sixteen agents in parallel. That frontier capability, delivered as a fully managed closed API, carries a premium price: $5 input and $30 output per million tokens. Kimi K2.7 trades some of that capability for radical affordability and openness — $0.95 input and $4.00 output per million, plus downloadable weights you can self-host. Whether Sol's premium is worth roughly five to seven times the token cost depends entirely on whether your workload actually uses the extra capability, or whether Kimi's cheaper, open floor already clears your bar.

What are the alternatives to GPT-5.6 Sol and Kimi K2.7?

Several sit close by on both sides of this closed-versus-open divide. Among open-weight rivals to Kimi, DeepSeek V4 is another 1-trillion-parameter Chinese open model with a 1M context and even cheaper output, and our Kimi K2.7 versus DeepSeek V4 comparison covers that matchup directly. Among closed flagships near Sol, Claude Opus 4.8 is a coding-first rival with an independently verified SWE-bench Verified score, and OpenAI's own GPT-5.5 remains active as a cheaper closed option. Kimi itself has been compared against GPT-5.5 and Claude Opus 4.8 in our library. For the mechanics behind these rate cards, our AI model pricing explainer breaks down input, output, and cached-token billing, and our guide to closed versus open-weight models walks through how to choose between the two camps for your own workload.

Final Verdict — Capability vs Cost and Control, a True Split

After running GPT-5.6 Sol through our own OpenAI API key, verifying both rate cards on the vendors' own documentation, and holding every capability claim to the one independent benchmark the two share, our verdict is a genuine split — and a clean one, because Sol and Kimi K2.7 barely compete for the same job. GPT-5.6 Sol is the capability, context, and speed leader: 59 to 42 on the Artificial Analysis Intelligence Index, No.1 at 80 on the Coding Agent Index where Kimi is not listed, a 1,050,000-token context to Kimi's 256K, and faster output. Kimi K2.7 is the price and openness leader: roughly five to seven and a half times cheaper per token, open weights under a Modified MIT license you can self-host today, and a native vision encoder Sol lacks. Both skip the independent SWE-bench Verified leaderboard, and each vendor's other headline coding numbers are self-reported in its own harness.

We did not crown a single overall winner because the evidence does not support one honestly: Sol's capability lead is real and wide, but it comes at multiples of the price and with no self-host option; Kimi's cost and openness are decisive, but they cannot buy the top of the measured intelligence and coding curves. If your work is the hardest reasoning, the largest context, or the most demanding agentic coding — pick GPT-5.6 Sol and pay for the ceiling. If your priority is cost, data control, or self-hosting — pick Kimi K2.7 and bank the difference. Because both expose an OpenAI-compatible surface, the pragmatic endgame for many teams is a split stack: run the volume on Kimi, and route only the tasks that measurably need the extra headroom to Sol. For the models and rivals around this matchup, see our GPT-5.6 Sol review, our Kimi K2.7 review, our DeepSeek V4 review, our Kimi K2.7 vs DeepSeek V4 comparison, our Kimi K2.7 vs GPT-5.5 comparison, and our Claude Opus 4.8 vs Kimi K2.7 comparison.

Sources

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

• OpenAI — GPT-5.6 announcement, tiers, and positioning
• OpenAI — GPT-5.6 Sol model documentation and specifications
• OpenAI — GPT-5.6 Sol API pricing
• Moonshot AI — Kimi K2.7 platform pricing and specifications
• Artificial Analysis — Kimi K2.7 Intelligence Index, output speed, and pricing
• Artificial Analysis — Intelligence Index and Coding Agent Index methodology
• Moonshot AI — Kimi K2.7 open weights on HuggingFace
• LMArena — human-preference Elo leaderboard

Last compared: July 2026. GPT-5.6 Sol reached general availability on July 9, 2026, and Kimi K2.7 was announced on June 12, 2026; both are recent, and we will revise this comparison as independent benchmark coverage matures.