o3-pro vs Kimi K2.7 Code vs Claude Opus 4.1
Kimi K2.7 Code comes out ahead, 64 to 51 and 49 on our weighted score, and it is the cheaper option too.
OpenAIo3-pro
51/100- ECI147.4
- Price$20.00 / $80.00
- Context200K
- Our pick
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
AnthropicClaude Opus 4.1
49/100- ECI144.1
- Price$15.00 / $75.00
- Context200K
Kimi K2.7 Code is our pick
Kimi K2.7 Code is the better all-round choice, scoring 64/100 against o3-pro (51) and Claude Opus 4.1 (49). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2.7 CodeCapabilities Index (ECI): Kimi K2.7 Code 150.0 · o3-pro 147.4 · Claude Opus 4.1 144.1
- Lowest priceKimi K2.7 CodeKimi K2.7 Code $1.71 · Claude Opus 4.1 $30.00 · o3-pro $35.00 per 1M tokens (3:1 blend)
- Longest contextKimi K2.7 CodeKimi K2.7 Code 262,144 · o3-pro 200,000 · Claude Opus 4.1 200,000 tokens
- Widest inputsKimi K2.7 Code and Claude Opus 4.1o3-pro: Text, Images · Kimi K2.7 Code: Text, Images, Video · Claude Opus 4.1: Text, Images, PDFs
- Self-hostingKimi K2.7 CodePublishes downloadable weights
| Measure | Weight | o3-pro | Kimi K2.7 Code | Claude Opus 4.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 75 | 78 | 71 |
| Price | 25% | 0 | 39 | 0 |
| Inputs & features | 15% | 70 | 80 | 70 |
| Context window | 10% | 32 | 37 | 32 |
| Overall | 100% | 51/100 | 64/100 | 49/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | o3-proOpenAI | Kimi K2.7 CodeMoonshot AI | Claude Opus 4.1Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 147.4 | 150.0 (best) | 144.1 |
| ECI rank | #60 of 148 | #49 of 148 (best) | #81 of 148 |
| GPQA DiamondGraduate-level science questions | — | 87.9% (best) | 77.3% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 54.0% (best) | 12.6% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 95.6% (best) | 68.9% |
| SWE-bench VerifiedFixing real GitHub issues | — | — | 73.4% |
| SimpleQA VerifiedShort factual questions | — | 36.5% | — |
| Price per million tokens | |||
| Input | $20.00 | $0.95 (best) | $15.00 |
| Output | $80.00 | $4.00 (best) | $75.00 |
| Cached input | — | $0.19 | — |
| Blended (3:1) | $35.00 | $1.71 (best) | $30.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official Moonshot AI API | Median of 14 providers |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 200,000 tokens |
| Max output | 100,000 tokens | 262,144 tokens (best) | 32,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yeslow · medium · high | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | o3-pro | kimi-k2.7-code | — |
| API providers | 6 | 51 (best) | 14 |
| Released | Jun 10, 2025 | Jun 12, 2026 | Aug 5, 2025 |
| Knowledge cutoff | May 2024 | Jan 2025 | Mar 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o3-pro$360.00
- Kimi K2.7 Code$17.50
- Claude Opus 4.1$300.00
Which should you choose?
Which is better: o3-pro, Kimi K2.7 Code or Claude Opus 4.1?
Kimi K2.7 Code is the better all-round choice, scoring 64/100 against o3-pro (51) and Claude Opus 4.1 (49). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, o3-pro, Kimi K2.7 Code or Claude Opus 4.1?
Kimi K2.7 Code is cheaper at $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). Claude Opus 4.1 costs $15.00 input / $75.00 output per million tokens (median across 14 API providers); o3-pro costs $20.00 input / $80.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $1.71 per million tokens for Kimi K2.7 Code versus $30.00 for Claude Opus 4.1 (18× as much) and $35.00 for o3-pro (20× as much).
Which scores higher on benchmarks?
Kimi K2.7 Code scores higher on the Capabilities Index (ECI): Kimi K2.7 Code 150.0 (#49 of 148), o3-pro 147.4 (#60 of 148) and Claude Opus 4.1 144.1 (#81 of 148). The confidence ranges of the top two overlap (148.1–151.8 vs 145.8–149.7), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for o3-pro and Kimi K2.7 Code yet, so there is no like-for-like coding score. On overall capability, Kimi K2.7 Code leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Kimi K2.7 Code has the largest context window at 262,144 tokens, against 200,000 for o3-pro and 200,000 for Claude Opus 4.1. Maximum output per response: o3-pro up to 100,000, Kimi K2.7 Code up to 262,144, Claude Opus 4.1 up to 32,000 tokens.
Which can read images, PDFs, audio or video?
o3-pro accepts text and images; Kimi K2.7 Code accepts text, images and video; Claude Opus 4.1 accepts text, images and PDFs. Kimi K2.7 Code handles the widest range of inputs.
Are any of these open source?
Kimi K2.7 Code publishes its weights and can be self-hosted; o3-pro and Claude Opus 4.1 is proprietary.
Which is newer?
Kimi K2.7 Code is the newest, released Jun 12, 2026. Claude Opus 4.1 came out Aug 5, 2025; o3-pro came out Jun 10, 2025. Knowledge cutoff: o3-pro May 2024, Kimi K2.7 Code Jan 2025, Claude Opus 4.1 Mar 31, 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
