Model comparison
GPT-5.6 Sol vs Kimi K3
OpenAI against Moonshot AI, compared on context, price, and verified benchmark results.
Catalog record checked August 10, 2026; individual provider fields may change.
OpenAI
GPT-5.6 Sol
Frontier
Moonshot AI
Kimi K3
Frontier · Open weights
| Specification | GPT-5.6 Sol | Kimi K3 |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Tier | Frontier | Frontier |
| Context window | Winner: 1.05M | 1.05M |
| Max output | 128K | Winner: 1M |
| Input / 1M tokens | $5 | Winner: $3 |
| Output / 1M tokens | $30 | Winner: $15 |
| Weights | Closed | Open |
| Parameters | Not disclosed | 2.8T total / 104B active (MoE) |
| Reasoning levels | none, low, medium, high, xhigh, max | low, high, max |
| Modalities | text, image | text, image, video |
| Released | July 9, 2026 | July 16, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-08) | Winner: 61 | 60 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: GPT-5.6 Sol: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.
GPT-5.6 Sol
OpenAI's flagship tier, aimed at the hardest reasoning and agentic work.
Best for
- Complex reasoning
- Agentic workflows
- Hard coding tasks
Watch out
25x Luna's input price — overspecified for routine generation. Sol Fast mode (where offered) is roughly ~2× price for ~2.5× speed — only enable when latency is the constraint. `gpt-5.2-chat-latest` / `gpt-5.3-chat-latest` shut down 2026-08-10 — migrate API chat traffic to Sol. ChatGPT Plus/Pro Chat got an Aug 6 Sol refresh + effort slider; Work/Codex/API July builds are unchanged.
Kimi K3
Open-weight frontier model scoring within a few points of the closed leaders on agentic coding.
Best for
- Agentic coding without vendor lock-in
- Self-hosting at frontier quality
- Long-context work
Watch out
2.8T parameters means self-hosting is a datacentre exercise, not a workstation one — open weights here mean provider choice, not local inference.
Benchmark
DeepSWE 1.1 in context
Both models shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
GPT-5.6 Sol vs Kimi K3
Answered from the verified figures on this page rather than general guidance.
Is GPT-5.6 Sol or Kimi K3 cheaper for input?
Kimi K3 is cheaper at $3 per million input tokens, against $5 for GPT-5.6 Sol — roughly 1.7× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.6 Sol has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.
Is GPT-5.6 Sol or Kimi K3 cheaper for output?
Kimi K3 is cheaper at $15 per million output tokens, against $30 for GPT-5.6 Sol — roughly 2.0× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.6 Sol has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.
Which has the larger context window, GPT-5.6 Sol or Kimi K3?
Both accept about 1.05M tokens of context, so document length will not decide between them.
Do GPT-5.6 Sol and Kimi K3 support the same reasoning levels?
GPT-5.6 Sol exposes none, low, medium, high, xhigh, max, while Kimi K3 exposes low, high, max.
Should I use GPT-5.6 Sol or Kimi K3?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GPT-5.6 Sol suits complex reasoning; Kimi K3 suits agentic coding without vendor lock-in.
Can I self-host GPT-5.6 Sol or Kimi K3?
Kimi K3 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. GPT-5.6 Sol is a closed model whose supported access paths are controlled by its provider.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.