Kimi K2.7 Code vs Gemini 3.1 Pro Preview
Gemini 3.1 Pro Preview comes out ahead, 68 to 64 on our weighted score, though Kimi K2.7 Code is 2.6× cheaper per token.
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
- Our pick
GoogleGemini 3.1 Pro Preview
68/100- ECI154.8
- Price$2.00 / $12.00
- Context1.05M
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Gemini 3.1 Pro Preview is our pick
Gemini 3.1 Pro Preview is the better all-round choice, scoring 68/100 against Kimi K2.7 Code (64). It leads on capability, inputs & features and context window. Kimi K2.7 Code wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGemini 3.1 Pro PreviewCapabilities Index (ECI): Gemini 3.1 Pro Preview 154.8 · Kimi K2.7 Code 150.0
- Lowest priceKimi K2.7 CodeKimi K2.7 Code $1.71 · Gemini 3.1 Pro Preview $4.50 per 1M tokens (3:1 blend)
- Longest contextGemini 3.1 Pro PreviewGemini 3.1 Pro Preview 1,048,576 · Kimi K2.7 Code 262,144 tokens
- Widest inputsGemini 3.1 Pro PreviewKimi K2.7 Code: Text, Images, Video · Gemini 3.1 Pro Preview: Text, Images, PDFs, Audio, Video
- Self-hostingKimi K2.7 CodePublishes downloadable weights
| Measure | Weight | Kimi K2.7 Code | Gemini 3.1 Pro Preview |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 84 |
| Price | 25% | 39 | 19 |
| Inputs & features | 15% | 80 | 100 |
| Context window | 10% | 37 | 61 |
| Overall | 100% | 64/100 | 68/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Kimi K2.7 CodeMoonshot AI | Gemini 3.1 Pro PreviewGoogle |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 150.0 | 154.8 (best) |
| ECI rank | #49 of 148 | #31 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 87.9% | 94.4% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 54.0% | 59.7% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 95.6% | 95.6% (best) |
| SWE-bench VerifiedFixing real GitHub issues | — | 75.6% |
| SimpleQA VerifiedShort factual questions | 36.5% | 73.5% (best) |
| Price per million tokens | ||
| Input | $0.95 (best) | $2.00 |
| Output | $4.00 (best) | $12.00 |
| Cached input | $0.19 (best) | $0.20 |
| Blended (3:1) | $1.71 (best) | $4.50 |
| Long-context rate | Same rate | Over 200K: $4.00 / $18.00 |
| Price source | Official Moonshot AI API | Official Google API |
| Limits | ||
| Context window | 262,144 tokens | 1,048,576 tokens (best) |
| Max output | 262,144 tokens (best) | 65,536 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | Yes |
| Audio | No | Yes |
| Video | Yes | Yes |
| Reasoning | Yes | Yeslow · medium · high |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Open | Proprietary |
| API model ID | kimi-k2.7-code | gemini-3.1-pro-preview |
| API providers | 51 (best) | 26 |
| Released | Jun 12, 2026 | Feb 19, 2026 |
| Knowledge cutoff | Jan 2025 | Jan 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Kimi K2.7 Code$17.50
- Gemini 3.1 Pro Preview$44.00
Which should you choose?
Which is better: Kimi K2.7 Code or Gemini 3.1 Pro Preview?
Gemini 3.1 Pro Preview is the better all-round choice, scoring 68/100 against Kimi K2.7 Code (64). It leads on capability, inputs & features and context window. Kimi K2.7 Code wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Kimi K2.7 Code or Gemini 3.1 Pro Preview?
Kimi K2.7 Code is cheaper at $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). Gemini 3.1 Pro Preview costs $2.00 input / $12.00 output per million tokens (official Google 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 $4.50 for Gemini 3.1 Pro Preview (2.6× as much).
Which scores higher on benchmarks?
Gemini 3.1 Pro Preview scores higher on the Capabilities Index (ECI): Gemini 3.1 Pro Preview 154.8 (#31 of 148) and Kimi K2.7 Code 150.0 (#49 of 148). Their confidence ranges do not overlap (152.6–157.3 vs 148.1–151.8), so the gap is a real one. On individual benchmarks: GPQA Diamond — Gemini 3.1 Pro Preview 94.4%, Kimi K2.7 Code 87.9%; FrontierMath Tiers 1–3 — Gemini 3.1 Pro Preview 59.7%, Kimi K2.7 Code 54.0%; OTIS Mock AIME 2024–2025 — Gemini 3.1 Pro Preview 95.6%, Kimi K2.7 Code 95.6%; SimpleQA Verified — Gemini 3.1 Pro Preview 73.5%, Kimi K2.7 Code 36.5%.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2.7 Code yet, so there is no like-for-like coding score. On overall capability, Gemini 3.1 Pro Preview leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
Gemini 3.1 Pro Preview has the largest context window at 1,048,576 tokens, against 262,144 for Kimi K2.7 Code. Maximum output per response: Kimi K2.7 Code up to 262,144, Gemini 3.1 Pro Preview up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Kimi K2.7 Code accepts text, images and video; Gemini 3.1 Pro Preview accepts text, images, PDFs, audio and video. Gemini 3.1 Pro Preview handles the widest range of inputs.
Are any of these open source?
Kimi K2.7 Code publishes its weights and can be self-hosted; Gemini 3.1 Pro Preview is proprietary.
Which is newer?
Kimi K2.7 Code is the newest, released Jun 12, 2026. Gemini 3.1 Pro Preview came out Feb 19, 2026. Knowledge cutoff: Kimi K2.7 Code Jan 2025, Gemini 3.1 Pro Preview Jan 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.
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