Mistral Large 2.1 vs Mistral Nemo vs Qwen2.5 32B Instruct
Mistral Nemo comes out ahead, 48 to 43 and 38 on our weighted score, and it is the cheaper option too.
Mistral AIMistral Large 2.1
38/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
- Our pickMistral AI
Mistral Nemo
48/100- ECI118.7
- Price$0.15 / $0.15
- Context128K
Alibaba (Qwen)Qwen2.5 32B Instruct
43/100- ECI128.5
- Price$0.70 / $2.80
- Context131K
Mistral Nemo is our pick
Mistral Nemo is the better all-round choice, scoring 48/100 against Qwen2.5 32B Instruct (43) and Mistral Large 2.1 (38). It leads on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMistral Large 2.1 and Qwen2.5 32B InstructCapabilities Index (ECI): Mistral Large 2.1 128.5 · Qwen2.5 32B Instruct 128.5 · Mistral Nemo 118.7
- Lowest priceMistral NemoMistral Nemo $0.15 · Qwen2.5 32B Instruct $1.23 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextMistral Large 2.1 and Qwen2.5 32B InstructMistral Large 2.1 131,072 · Qwen2.5 32B Instruct 131,072 · Mistral Nemo 128,000 tokens
- Widest inputsSame inputsMistral Large 2.1: Text · Mistral Nemo: Text · Qwen2.5 32B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mistral Large 2.1 | Mistral Nemo | Qwen2.5 32B Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 51 | 39 | 51 |
| Price | 25% | 27 | 89 | 46 |
| Inputs & features | 15% | 25 | 25 | 25 |
| Context window | 10% | 24 | 24 | 24 |
| Overall | 100% | 38/100 | 48/100 | 43/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mistral Large 2.1Mistral AI | Mistral NemoMistral AI | Qwen2.5 32B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 128.5 (best) | 118.7 | 128.5 (best) |
| ECI rank | #130 of 148 (best) | #140 of 148 | #131 of 148 |
| GPQA DiamondGraduate-level science questions | 51.3% (best) | 29.9% | 46.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.8% (best) | — | 7.4% |
| Price per million tokens | |||
| Input | $2.00 | $0.15 (best) | $0.70 |
| Output | $6.00 | $0.15 (best) | $2.80 |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | $0.15 (best) | $1.23 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Mistral API | Official Alibaba API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 16,384 tokens | 128,000 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | 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 | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | mistral-large-2411 | mistral-nemo | qwen2-5-32b-instruct |
| API providers | 2 | 5 (best) | 1 |
| Released | Nov 18, 2024 | Jul 1, 2024 | Sep 17, 2024 |
| Knowledge cutoff | Nov 2024 | Jul 2024 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mistral Large 2.1$32.00
- Mistral Nemo$1.80
- Qwen2.5 32B Instruct$12.60
Which should you choose?
Which is better: Mistral Large 2.1, Mistral Nemo or Qwen2.5 32B Instruct?
Mistral Nemo is the better all-round choice, scoring 48/100 against Qwen2.5 32B Instruct (43) and Mistral Large 2.1 (38). It leads on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Mistral Large 2.1, Mistral Nemo or Qwen2.5 32B Instruct?
Mistral Nemo is cheaper at $0.15 input / $0.15 output per million tokens (official Mistral API price). Qwen2.5 32B Instruct costs $0.70 input / $2.80 output per million tokens (official Alibaba API price); Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (official Mistral 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 $1.23 for Qwen2.5 32B Instruct (8.2× as much) and $3.00 for Mistral Large 2.1 (20× as much).
Which scores higher on benchmarks?
Mistral Large 2.1 scores higher on the Capabilities Index (ECI): Mistral Large 2.1 128.5 (#130 of 148), Qwen2.5 32B Instruct 128.5 (#131 of 148) and Mistral Nemo 118.7 (#140 of 148). The confidence ranges of the top two overlap (123.8–130.8 vs 123.5–130.0), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Qwen2.5 32B Instruct 46.1%, Mistral Nemo 29.9%.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Large 2.1, Mistral Nemo and Qwen2.5 32B Instruct yet, so there is no like-for-like coding score. On overall capability, Mistral Large 2.1 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?
Mistral Large 2.1 and Qwen2.5 32B Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Mistral Nemo. Maximum output per response: Mistral Large 2.1 up to 16,384, Mistral Nemo up to 128,000, Qwen2.5 32B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Mistral Large 2.1 accepts text; Mistral Nemo accepts text; Qwen2.5 32B Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Mistral Large 2.1 is the newest, released Nov 18, 2024. Qwen2.5 32B Instruct came out Sep 17, 2024; Mistral Nemo came out Jul 1, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, Mistral Nemo Jul 2024, Qwen2.5 32B Instruct Apr 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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