Qwen2.5 7B Instruct vs Mistral Small 3.1 24B vs Mistral Nemo
Mistral Small 3.1 24B comes out ahead, 55 to 48 and 44 on our weighted score, though Mistral Nemo is 47% cheaper per token.
Alibaba (Qwen)Qwen2.5 7B Instruct
44/100- ECI118.5
- Price$0.175 / $0.70
- Context131K
- Our pick
Mistral AIMistral Small 3.1 24B
55/100- ECI127.5
- Price$0.229 / $0.436
- Context128K
- Mistral AI
Mistral Nemo
48/100- ECI118.7
- Price$0.15 / $0.15
- Context128K
Mistral Small 3.1 24B is our pick
Mistral Small 3.1 24B is the better all-round choice, scoring 55/100 against Mistral Nemo (48) and Qwen2.5 7B Instruct (44). It leads on capability and inputs & features. Mistral Nemo wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMistral Small 3.1 24BCapabilities Index (ECI): Mistral Small 3.1 24B 127.5 · Mistral Nemo 118.7 · Qwen2.5 7B Instruct 118.5
- Lowest priceMistral NemoMistral Nemo $0.15 · Mistral Small 3.1 24B $0.281 · Qwen2.5 7B Instruct $0.306 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Mistral Small 3.1 24B 128,000 · Mistral Nemo 128,000 tokens
- Widest inputsMistral Small 3.1 24BQwen2.5 7B Instruct: Text · Mistral Small 3.1 24B: Text, Images · Mistral Nemo: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5 7B Instruct | Mistral Small 3.1 24B | Mistral Nemo |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 38 | 50 | 39 |
| Price | 25% | 74 | 76 | 89 |
| Inputs & features | 15% | 25 | 60 | 25 |
| Context window | 10% | 24 | 24 | 24 |
| Overall | 100% | 44/100 | 55/100 | 48/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen2.5 7B InstructAlibaba (Qwen) | Mistral Small 3.1 24BMistral AI | Mistral NemoMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 118.5 | 127.5 (best) | 118.7 |
| ECI rank | #141 of 148 | #132 of 148 (best) | #140 of 148 |
| GPQA DiamondGraduate-level science questions | 35.5% | 47.5% (best) | 29.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | 2.5% | 5.8% (best) | — |
| Price per million tokens | |||
| Input | $0.175 | $0.229 | $0.15 (best) |
| Output | $0.70 | $0.436 | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.306 | $0.281 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 2 providers | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 128,000 tokens |
| Max output | 8,192 tokens | 16,384 tokens | 128,000 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 | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | qwen2-5-7b-instruct | — | mistral-nemo |
| API providers | 1 | 2 | 5 (best) |
| Released | Sep 19, 2024 | Mar 17, 2025 | Jul 1, 2024 |
| Knowledge cutoff | Apr 2024 | Jun 2024 | Jul 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen2.5 7B Instruct$3.15
- Mistral Small 3.1 24B$3.16
- Mistral Nemo$1.80
Which should you choose?
Which is better: Qwen2.5 7B Instruct, Mistral Small 3.1 24B or Mistral Nemo?
Mistral Small 3.1 24B is the better all-round choice, scoring 55/100 against Mistral Nemo (48) and Qwen2.5 7B Instruct (44). It leads on capability and inputs & features. Mistral Nemo wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen2.5 7B Instruct, Mistral Small 3.1 24B or Mistral Nemo?
Mistral Nemo is cheaper at $0.15 input / $0.15 output per million tokens (official Mistral API price). Mistral Small 3.1 24B costs $0.229 input / $0.436 output per million tokens (median across 2 API providers); Qwen2.5 7B Instruct costs $0.175 input / $0.70 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Mistral Nemo versus $0.281 for Mistral Small 3.1 24B (1.9× as much) and $0.306 for Qwen2.5 7B Instruct (2× as much).
Which scores higher on benchmarks?
Mistral Small 3.1 24B scores higher on the Capabilities Index (ECI): Mistral Small 3.1 24B 127.5 (#132 of 148), Mistral Nemo 118.7 (#140 of 148) and Qwen2.5 7B Instruct 118.5 (#141 of 148). Their confidence ranges do not overlap (122.6–129.4 vs 111.3–121.5), so the gap is a real one. On individual benchmarks: GPQA Diamond — Mistral Small 3.1 24B 47.5%, Qwen2.5 7B Instruct 35.5%, Mistral Nemo 29.9%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5 7B Instruct, Mistral Small 3.1 24B and Mistral Nemo yet, so there is no like-for-like coding score. On overall capability, Mistral Small 3.1 24B 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?
Qwen2.5 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for Mistral Small 3.1 24B and 128,000 for Mistral Nemo. Maximum output per response: Qwen2.5 7B Instruct up to 8,192, Mistral Small 3.1 24B up to 16,384, Mistral Nemo up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5 7B Instruct accepts text; Mistral Small 3.1 24B accepts text and images; Mistral Nemo accepts text. Mistral Small 3.1 24B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Mistral Small 3.1 24B is the newest, released Mar 17, 2025. Qwen2.5 7B Instruct came out Sep 19, 2024; Mistral Nemo came out Jul 1, 2024. Knowledge cutoff: Qwen2.5 7B Instruct Apr 2024, Mistral Small 3.1 24B Jun 2024, Mistral Nemo Jul 2024.
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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