Mistral Large 2.1 vs Qwen3.8 27B
Qwen3.8 27B comes out ahead, 66 to 38 on our weighted score, and it is the cheaper option too.
Mistral AIMistral Large 2.1
38/100- ECI128.6
- Price$2.00 / $6.00
- Context131K
- Our pick
Alibaba (Qwen)Qwen3.8 27B
66/100- ECI151.8
- Price$0.40 / $2.55
- Context262K
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Make it a three-way comparison.
Qwen3.8 27B is our pick
Qwen3.8 27B is the better all-round choice, scoring 66/100 against Mistral Large 2.1 (38). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.8 27BCapabilities Index (ECI): Qwen3.8 27B 151.8 · Mistral Large 2.1 128.6
- Lowest priceQwen3.8 27BQwen3.8 27B $0.938 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextQwen3.8 27BQwen3.8 27B 262,144 · Mistral Large 2.1 131,072 tokens
- Widest inputsQwen3.8 27BMistral Large 2.1: Text · Qwen3.8 27B: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mistral Large 2.1 | Qwen3.8 27B |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 50 | 79 |
| Price | 25% | 26 | 49 |
| Inputs & features | 15% | 25 | 70 |
| Context window | 10% | 25 | 37 |
| Overall | 100% | 38/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mistral Large 2.1Mistral AI | Qwen3.8 27BAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 128.6 | 151.8 (best) |
| ECI rank | #142 of 165 | #54 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 51.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.8% | — |
| Price per million tokens | ||
| Input | $2.00 | $0.40 (best) |
| Output | $6.00 | $2.55 (best) |
| Cached input | — | — |
| Blended (3:1) | $3.00 | $0.938 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 49 providers |
| Limits | ||
| Context window | 131,072 tokens | 262,144 tokens (best) |
| Max output | 16,384 tokens | 32,768 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | Yes |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | — | — |
| API providers | 2 | 53 (best) |
| Released | Nov 18, 2024 | Aug 14, 2026 |
| Knowledge cutoff | Nov 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
- Qwen3.8 27B$9.10
Which should you choose?
Which is better: Mistral Large 2.1 or Qwen3.8 27B?
Qwen3.8 27B is the better all-round choice, scoring 66/100 against Mistral Large 2.1 (38). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Mistral Large 2.1 or Qwen3.8 27B?
Qwen3.8 27B is cheaper at $0.40 input / $2.55 output per million tokens (median across 49 API providers). Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.938 per million tokens for Qwen3.8 27B versus $3.00 for Mistral Large 2.1 (3.2× as much).
Which scores higher on benchmarks?
Qwen3.8 27B scores higher on the Capabilities Index (ECI): Qwen3.8 27B 151.8 (#54 of 165) and Mistral Large 2.1 128.6 (#142 of 165). Their confidence ranges do not overlap (149.9–153.6 vs 124.3–130.7), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Large 2.1 and Qwen3.8 27B yet, so there is no like-for-like coding score. On overall capability, Qwen3.8 27B 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?
Qwen3.8 27B has the largest context window at 262,144 tokens, against 131,072 for Mistral Large 2.1. Maximum output per response: Mistral Large 2.1 up to 16,384, Qwen3.8 27B up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Mistral Large 2.1 accepts text; Qwen3.8 27B accepts text, images and video. Qwen3.8 27B handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
Qwen3.8 27B is the newest, released Aug 14, 2026. Mistral Large 2.1 came out Nov 18, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 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 Nov 2, 2026.
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