MiniMax-M2 Her vs Mistral Small 3.2 vs Qwen3 Coder Next
Mistral Small 3.2 comes out ahead, 64 to 51 and 45 on our weighted score, and it is the cheaper option too.
MiniMaxMiniMax-M2 Her
45/100- ECI—
- Price$0.30 / $1.20
- Context66K
- Our pick
Mistral AIMistral Small 3.2
64/100- ECI131.7
- Price$0.10 / $0.30
- Context128K
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
Mistral Small 3.2 is our pick
Mistral Small 3.2 is the better all-round choice, scoring 64/100 against Qwen3 Coder Next (51) and MiniMax-M2 Her (45). It leads on price and inputs & features. Qwen3 Coder Next wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceMistral Small 3.2Mistral Small 3.2 $0.15 · Qwen3 Coder Next $0.45 · MiniMax-M2 Her $0.525 per 1M tokens (3:1 blend)
- Longest contextQwen3 Coder NextQwen3 Coder Next 262,144 · Mistral Small 3.2 128,000 · MiniMax-M2 Her 65,536 tokens
- Widest inputsMistral Small 3.2MiniMax-M2 Her: Text · Mistral Small 3.2: Text, Images · Qwen3 Coder Next: Text
- Self-hostingMistral Small 3.2 and Qwen3 Coder NextPublishes downloadable weights
| Measure | Weight | MiniMax-M2 Her | Mistral Small 3.2 | Qwen3 Coder Next |
|---|---|---|---|---|
| Price | 50% | 63 | 89 | 66 |
| Inputs & features | 30% | 35 | 50 | 35 |
| Context window | 20% | 12 | 24 | 37 |
| Overall | 100% | 45/100 | 64/100 | 51/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M2 HerMiniMax | Mistral Small 3.2Mistral AI | Qwen3 Coder NextAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 131.7 | — |
| ECI rank | — | #123 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 49.1% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 30.3% | — |
| Price per million tokens | |||
| Input | $0.30 | $0.10 (best) | $0.20 |
| Output | $1.20 | $0.30 (best) | $1.20 |
| Cached input | — | — | — |
| Blended (3:1) | $0.525 | $0.15 (best) | $0.45 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 4 providers | Official Mistral API | Median of 11 providers |
| Limits | |||
| Context window | 65,536 tokens | 128,000 tokens | 262,144 tokens (best) |
| Max output | 2,048 tokens | 16,384 tokens | 65,536 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | mistral-small-2506 | — |
| API providers | 4 | 6 | 11 (best) |
| Released | Jan 23, 2026 | Jun 20, 2025 | Feb 3, 2026 |
| Knowledge cutoff | — | Mar 2025 | Sep 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 Her$5.40
- Mistral Small 3.2$1.60
- Qwen3 Coder Next$4.40
Which should you choose?
Which is better: MiniMax-M2 Her, Mistral Small 3.2 or Qwen3 Coder Next?
Mistral Small 3.2 is the better all-round choice, scoring 64/100 against Qwen3 Coder Next (51) and MiniMax-M2 Her (45). It leads on price and inputs & features. Qwen3 Coder Next wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, MiniMax-M2 Her, Mistral Small 3.2 or Qwen3 Coder Next?
Mistral Small 3.2 is cheaper at $0.10 input / $0.30 output per million tokens (official Mistral API price). Qwen3 Coder Next costs $0.20 input / $1.20 output per million tokens (median across 11 API providers); MiniMax-M2 Her costs $0.30 input / $1.20 output per million tokens (median across 4 API providers). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Mistral Small 3.2 versus $0.45 for Qwen3 Coder Next (3× as much) and $0.525 for MiniMax-M2 Her (3.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiniMax-M2 Her has not been scored yet, Mistral Small 3.2 has an ECI of 131.7 and Qwen3 Coder Next has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2 Her, Mistral Small 3.2 and Qwen3 Coder Next yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3 Coder Next has the largest context window at 262,144 tokens, against 128,000 for Mistral Small 3.2 and 65,536 for MiniMax-M2 Her. Maximum output per response: MiniMax-M2 Her up to 2,048, Mistral Small 3.2 up to 16,384, Qwen3 Coder Next up to 65,536 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2 Her accepts text; Mistral Small 3.2 accepts text and images; Qwen3 Coder Next accepts text. Mistral Small 3.2 handles the widest range of inputs.
Are any of these open source?
Mistral Small 3.2 and Qwen3 Coder Next publishes its weights and can be self-hosted; MiniMax-M2 Her is proprietary.
Which is newer?
Qwen3 Coder Next is the newest, released Feb 3, 2026. MiniMax-M2 Her came out Jan 23, 2026; Mistral Small 3.2 came out Jun 20, 2025. Knowledge cutoff: Mistral Small 3.2 Mar 2025, Qwen3 Coder Next Sep 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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