MiniMax-M2.7 vs Kimi K2.7 Code vs GPT-5.4 nano
GPT-5.4 nano comes out ahead, 68 to 64 and 61 on our weighted score, and it is the cheaper option too.
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
- Our pick
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
GPT-5.4 nano is our pick
GPT-5.4 nano is the better all-round choice, scoring 68/100 against Kimi K2.7 Code (64) and MiniMax-M2.7 (61). It leads on price and context window. Kimi K2.7 Code wins on capability and inputs & features. 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 · MiniMax-M2.7 145.9 · GPT-5.4 nano 145.8
- Lowest priceGPT-5.4 nanoGPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 · Kimi K2.7 Code $1.71 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · Kimi K2.7 Code 262,144 · MiniMax-M2.7 204,800 tokens
- Widest inputsKimi K2.7 CodeMiniMax-M2.7: Text · Kimi K2.7 Code: Text, Images, Video · GPT-5.4 nano: Text, Images
- Self-hostingMiniMax-M2.7 and Kimi K2.7 CodePublishes downloadable weights
| Measure | Weight | MiniMax-M2.7 | Kimi K2.7 Code | GPT-5.4 nano |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 78 | 73 |
| Price | 25% | 63 | 39 | 66 |
| Inputs & features | 15% | 35 | 80 | 70 |
| Context window | 10% | 32 | 37 | 44 |
| Overall | 100% | 61/100 | 64/100 | 68/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M2.7MiniMax | Kimi K2.7 CodeMoonshot AI | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.9 | 150.0 (best) | 145.8 |
| ECI rank | #73 of 148 | #49 of 148 (best) | #75 of 148 |
| GPQA DiamondGraduate-level science questions | — | 87.9% (best) | 78.5% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 54.0% (best) | 44.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 95.6% (best) | 87.8% |
| SimpleQA VerifiedShort factual questions | — | 36.5% (best) | 11.7% |
| Price per million tokens | |||
| Input | $0.30 | $0.95 | $0.20 (best) |
| Output | $1.20 (best) | $4.00 | $1.25 |
| Cached input | $0.06 | $0.19 | $0.02 (best) |
| Blended (3:1) | $0.525 | $1.71 | $0.463 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official MiniMax (minimax.io) API | Official Moonshot AI API | Official OpenAI API |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens | 400,000 tokens (best) |
| Max output | 131,072 tokens | 262,144 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yeslow · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | MiniMax-M2.7 | kimi-k2.7-code | gpt-5.4-nano |
| API providers | 29 | 51 (best) | 26 |
| Released | Mar 18, 2026 | Jun 12, 2026 | Mar 17, 2026 |
| Knowledge cutoff | — | Jan 2025 | Aug 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.
- MiniMax-M2.7$5.40
- Kimi K2.7 Code$17.50
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: MiniMax-M2.7, Kimi K2.7 Code or GPT-5.4 nano?
GPT-5.4 nano is the better all-round choice, scoring 68/100 against Kimi K2.7 Code (64) and MiniMax-M2.7 (61). It leads on price and context window. Kimi K2.7 Code wins on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, MiniMax-M2.7, Kimi K2.7 Code or GPT-5.4 nano?
GPT-5.4 nano is cheaper at $0.20 input / $1.25 output per million tokens (official OpenAI API price). MiniMax-M2.7 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price); Kimi K2.7 Code costs $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). At a typical mix of three input tokens to one output token, that is $0.463 per million tokens for GPT-5.4 nano versus $0.525 for MiniMax-M2.7 (1.1× as much) and $1.71 for Kimi K2.7 Code (3.7× 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), MiniMax-M2.7 145.9 (#73 of 148) and GPT-5.4 nano 145.8 (#75 of 148). Their confidence ranges do not overlap (148.1–151.8 vs 138.2–148.0), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2.7, Kimi K2.7 Code and GPT-5.4 nano 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?
GPT-5.4 nano has the largest context window at 400,000 tokens, against 262,144 for Kimi K2.7 Code and 204,800 for MiniMax-M2.7. Maximum output per response: MiniMax-M2.7 up to 131,072, Kimi K2.7 Code up to 262,144, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.7 accepts text; Kimi K2.7 Code accepts text, images and video; GPT-5.4 nano accepts text and images. Kimi K2.7 Code handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 and Kimi K2.7 Code publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.
Which is newer?
Kimi K2.7 Code is the newest, released Jun 12, 2026. MiniMax-M2.7 came out Mar 18, 2026; GPT-5.4 nano came out Mar 17, 2026. Knowledge cutoff: Kimi K2.7 Code Jan 2025, GPT-5.4 nano Aug 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.
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