Kimi K3 vs GPT-5.6 Sol
Kimi K3 is the largest open-weight model ever at 2.8 trillion parameters. GPT-5.6 Sol is OpenAI's new flagship — the top tier of the GPT-5.6 family, with an Ultra mode that hits 91.9% on Terminal-Bench 2.1. Sol leads on 6 of 9 shared benchmarks; K3 leads on 3 and costs 40% less per token. But the real story is how close they are: this is the first time an open-weight model has traded blows with OpenAI's best. Here's the full comparison using Moonshot's launch table, OpenAI's system card, and independent benchmarks.
Model cards side by side
| Specification | Kimi K3 | GPT-5.6 Sol |
|---|---|---|
| Provider / release | Moonshot AI · July 16, 2026 | OpenAI · July 9, 2026 GA |
| API model ID | kimi-k3 | gpt-5.6-sol (alias: gpt-5.6) |
| Architecture | MoE · 2.8T total / 16 of 896 experts active | Proprietary (undisclosed) |
| Attention mechanism | Kimi Delta Attention + Attention Residuals | Not publicly detailed |
| Context window | 1,048,576 tokens (1M) | 1.05M tokens |
| Maximum output | 131K default, up to 1,048,576 | 128K tokens |
| Reasoning modes | Max effort only at launch; lower modes planned | None through Max + Ultra mode (parallel multi-agent) |
| Input modalities | Text, image, video | Text, image |
| Tools | Function calling, structured outputs | Functions, web search, file search, computer use, MCP |
| Weights / license | Open weights (Modified MIT), promised by July 27 | Closed, proprietary, API-only |
| Input / output price | $3 / $15 per 1M | $5 / $30 per 1M |
| Cached input | $0.30 per 1M, automatic | $0.50 per 1M; writes at 1.25× input rate |
| Ultra / high-effort mode | Not available | Sol Ultra: 91.9% Terminal-Bench 2.1 |
| Speed | ~62 tok/s (Artificial Analysis) | ~53 tok/s; up to 750 tok/s on Cerebras |
Sol is the flagship of OpenAI's three-tier GPT-5.6 family (Sol, Terra, Luna). It ships with an Ultra mode that coordinates parallel sub-agents — the only model in this comparison with that capability. K3 counters with published architecture, planned open weights, native video input, and a 40% price advantage. Sol has the richer tool ecosystem (web search, file search, computer use); K3 has function calling and structured outputs.
Benchmarks: Sol leads on 6 of 9 shared, but K3 wins key agentic rows
The table below draws from Moonshot's official K3 launch comparison, OpenAI's GPT-5.6 system card, BenchLM shared results, and independent leaderboards. Harnesses differ per row — Moonshot uses KimiCode for K3 and Codex for Sol on several coding benchmarks. Treat small gaps as near-ties.
| Benchmark | Kimi K3 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| Terminal-Bench 2.1 | 88.3 | 88.8 | Sol +0.5 · near-tie; different harnesses |
| DeepSWE | 67.5 | 73.0 | Sol +5.5 · repository engineering |
| FrontierSWE | 81.2 | 71.3 | K3 +9.9 · largest K3 lead on any chart |
| BrowseComp | 91.2 | 90.4 | K3 +0.8 · narrow; Sol Ultra: 92.2 |
| GDPval-AA v2 (Elo) | 1,668 | 1,748 | Sol +80 Elo · professional knowledge work |
| GPQA Diamond | 93.5 | 94.6 | Sol +1.1 · both near ceiling |
| AutomationBench | 30.8 | 29.1 (AA: 51.2) | K3 +1.7 · different sources |
| Toolathlon-Verified | 73.2 | Not in Moonshot table | K3-only evidence |
| AA-Briefcase (Elo) | 1,548 | 1,495 | K3 +53 Elo · independent |
OpenAI's system card: what Sol brings that K3 doesn't
| Benchmark | GPT-5.6 Sol | Context |
|---|---|---|
| Terminal-Bench 2.1 (Ultra) | 91.9% | Parallel multi-agent; beats Mythos 5 (84.3%) and Fable 5 (83.4%) |
| SWE-bench Pro | 64.6% | #6 on public leaderboard; K3 not yet listed |
| Agents' Last Exam (ALE) | 53.6 | +13.1 vs Fable 5 (adaptive); K3 not scored |
| AA Coding Agent Index | 80.0 | +2.8 vs Fable 5; uses fewer tokens at lower cost |
| BrowseComp (Ultra) | 92.2% | Above K3's 91.2% |
| ExploitBench | 73.5% | Cybersecurity; K3 not scored |
| OSWorld 2.0 | 62.6% | Computer use; K3 not scored |
| HealthBench Professional | 60.5% | Clinical reasoning; K3 not scored |
Sol's Ultra mode is a genuine differentiator. At 91.9% on Terminal-Bench 2.1, it's the highest published score on that benchmark. The ALE score of 53.6 is 13.1 points above Fable 5. And Sol is notably token-efficient: Artificial Analysis measured ~15,000 output tokens per Intelligence Index task vs K3's much higher output volume, meaning Sol's higher per-token price is partially offset by using fewer tokens.
Radar: six shared benchmarks
● Kimi K3 ● GPT-5.6 Sol
The radar uses six benchmarks where both models have scores. Normalized: Term-Bench, DeepSWE, FrontierSWE, BrowseComp, GPQA to 100; GDPval-AA to 2000. It is a visual aid, not a new benchmark.
- Sol leads on Terminal-Bench 2.1, DeepSWE, GDPval-AA, and GPQA Diamond.
- K3 leads on FrontierSWE and BrowseComp.
- The radar shows how close these models are — the polygons nearly overlap on most axes.
- Sol's Ultra mode (91.9% Terminal-Bench) and ALE score (53.6) are not shown here but represent additional capability headroom.
Pricing: K3 costs 40% less, but Sol is more token-efficient
| Per 1M tokens | Kimi K3 | GPT-5.6 Sol |
|---|---|---|
| Fresh input / cache miss | $3.00 | $5.00 |
| Cached input / cache hit | $0.30 | $0.50 |
| Output | $15.00 | $30.00 |
| 10M fresh input + 2M output | $60.00 | $110.00 |
| 10M cached input + 2M output | $33.00 | $65.00 |
K3 is cheaper on every token class. But the real cost story is more nuanced. Artificial Analysis found K3 used roughly 130 million output tokens across its Intelligence Index evaluation, compared with Sol's ~70 million — nearly double. K3's output tokens cost half as much, but it used almost twice as many. Total evaluation spending ended up in a similar range. Sol is also more token-efficient per-task: ~15,000 output tokens per Intelligence Index task vs higher counts for K3.
The practical takeaway: cost per accepted task, not cost per token. K3's always-on max reasoning can consume more output tokens than expected. Sol's adjustable reasoning lets you spend less on routine work. Measure both on your actual workload.
Ecosystem & deployment
K3: open weights coming, API-only today
K3's full weights are promised by July 27 under a Modified MIT license. At 2.8T parameters with 16 active experts, self-hosting requires at least 64 accelerators. Until then, K3 is API-only through Moonshot's platform and Kimi products (Kimi.com, Kimi Work, Kimi Code).
The open-weight promise enables fine-tuning, air-gapped deployment, and sovereignty that closed models can't match. But the infrastructure burden is real — most teams will use an inference provider even after the weights land.
Sol: closed, but deeply integrated
Sol is available through ChatGPT, Codex, and the OpenAI API. It's part of a three-tier family: Sol (flagship, $5/$30), Terra (balanced, $2.50/$15), and Luna (fastest, $1/$6). This tiering lets you route simple tasks to cheaper models and reserve Sol for hard problems.
Sol also runs on Cerebras at up to 750 tok/s for select customers — a latency play for interactive agents. The OpenAI ecosystem (Codex CLI, web search, file search, computer use) is more mature than Moonshot's tooling.
Which model should you use?
Choose Kimi K3 if…
- You need the #1 frontend coding model — K3 leads the Frontend Code Arena.
- Your workload is long-horizon, terminal-heavy, or browse-intensive — K3 wins FrontierSWE (+9.9), BrowseComp, and AutomationBench.
- API cost is a first-order constraint — K3 is 40% cheaper at list price.
- You need video input, air-gapped deployment, or future open-weight flexibility.
- You want to run high-volume, measurable coding tasks with clear pass/fail criteria.
Choose GPT-5.6 Sol if…
- You need maximum coding capability — Sol leads DeepSWE (+5.5), SWE-bench Pro (64.6%), and Terminal-Bench 2.1.
- You want Ultra mode for the hardest problems — 91.9% Terminal-Bench 2.1 with parallel sub-agents.
- You value adjustable reasoning — dial effort from none through max per task.
- You rely on OpenAI's ecosystem: Codex, web search, file search, computer use, Cerebras speed.
- You want the GPT-5.6 family's tiered pricing — route simple tasks to Terra or Luna.
Compare both models on real code
Run the same repository prompt, tool schema, and acceptance tests rather than trusting a leaderboard headline.
Try both in CodingFleet →Final verdict
GPT-5.6 Sol is the stronger coding model. It leads on 6 of 9 shared benchmarks, posts the highest Terminal-Bench 2.1 score in Ultra mode (91.9%), and brings a mature ecosystem with adjustable reasoning, tiered pricing across the GPT-5.6 family, and deep Codex integration. For difficult repository engineering and controlled reasoning, it's the safer choice.
Kimi K3 is the more disruptive product. It wins on FrontierSWE (+9.9), BrowseComp, AutomationBench, and AA-Briefcase. It's #1 on the Frontend Code Arena. It costs 40% less at list price. And it will soon be available as open weights — a strategic advantage no closed model can match. For frontend coding, long-horizon agents, and cost-sensitive pipelines, it's extraordinarily compelling.
The honest assessment: Sol is the better model for hard engineering. K3 is the better deal for high-volume coding work. The gap between them is narrow enough — and the strengths are different enough — that the smartest architecture routes by task, not by model. Test both on your actual workload.
Sources and methodology
- Moonshot AI — Kimi K3 Tech Blog. Official launch table with benchmark scores and architecture details.
- OpenAI — GPT-5.6 system card and launch announcement. Official benchmark scores, pricing, and Ultra mode details.
- BenchLM — GPT-5.6 Sol vs Kimi K3. 25 shared benchmark results across 5 categories.
- LLM Stats — GPT-5.6 Sol vs Kimi K3. 9-benchmark comparison with pricing analysis.
- Artificial Analysis — K3 vs Sol comparison. Independent Intelligence Index, speed, and pricing measurements.
- MyClaw — Kimi K3 vs GPT-5.6 Sol. Coding, cost, and agent test analysis.
- CometAPI — GPT-5.6 Models Explained. Family overview with full benchmark table.
- AI Tools Review — GPT-5.6 Sol vs Terra vs Luna. Independent Artificial Analysis measurements.
- All benchmark rows retain their source labels and harness details. Provider-reported results can differ because of harness, prompt, context, and reasoning-budget choices. The radar and bar charts are visual aids, not statistically normalized leaderboards.