Model comparison
Mistral 7B vs Qwen3.8 27B
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 23.0 on the Noometry Index. Mistral 7B costs 4.5× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
Mistral AI
Alibaba (Qwen)Summary
- They share 18 benchmarks with published results for both. Mistral 7B scores higher in 0 categories and Qwen3.8 27B in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3.8 27B leads 65.8 to 30.7.
- The biggest single-benchmark swing is DTBench: 42.5% for Mistral 7B and 88% for Qwen3.8 27B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
- Qwen3.8 27B accepts more context: 262K tokens versus 8K.
Side by side
| Mistral 7B | Qwen3.8 27B | |
|---|---|---|
| Provider | Mistral AI | Alibaba (Qwen) |
| Noometry Index | 23.0 | 46.0 |
| Released | 2023-09-27 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 8K | 262K |
| Max output | 8K | 33K |
| Input $ / M tokens | $0.25 | $0.99 |
| Output $ / M tokens | $0.25 | $1.49 |
| Results tracked | 37 | 31 |
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Category by category
Coding Qwen3.8 27B leads
Mistral 7B: 26.4 (#326), Qwen3.8 27B: 50.5 (#44)
| Benchmark | Mistral 7B | Qwen3.8 27B |
|---|---|---|
| LMArena Coding | 1082 | 1482 |
| LMArena WebDev | — | 1593 |
| SciCode | — | 46.6% |
| BigCodeBench Instruct | 19.5% | — |
| BigCodeBench Complete | 27.3% | — |
| HumanEval+ | 36% | — |
| MBPP+ | 42.1% | — |
Agentic & Tool Use Not comparable
Mistral 7B: —, Qwen3.8 27B: 32.9 (#57)
| Benchmark | Mistral 7B | Qwen3.8 27B |
|---|---|---|
| APEX-Agents | — | 47.5% |
Reasoning Qwen3.8 27B leads
Mistral 7B: 13.1 (#336), Qwen3.8 27B: 41.0 (#54)
| Benchmark | Mistral 7B | Qwen3.8 27B |
|---|---|---|
| LMArena Hard Prompts | 1067 | 1460 |
| DTBench | 42.5% | 88% |
| Epoch Capabilities Index | 112.21 | 149.38 |
| ARC-AGI-2 | — | 42.4% |
| NYT Connections (extended) | — | 54.5% |
| ARC-AGI-1 | — | 87.5% |
| CritPt | — | 5.4% |
| Chess Puzzles | 0% | — |
| LMCA | — | 41.4% |
| Surface Evolver Bench | — | 45% |
| Adversarial NLI | 47.1% | — |
| BIG-Bench Hard | 56.1% | — |
| HellaSwag | 81% | — |
| PIQA | 83% | — |
| WinoGrande | 75.3% | — |
Math Qwen3.8 27B leads
Mistral 7B: 8.1 (#325), Qwen3.8 27B: 37.1 (#161)
| Benchmark | Mistral 7B | Qwen3.8 27B |
|---|---|---|
| LMArena Math | 1085 | 1456 |
| OTIS Mock AIME 2024-2025 | 0.3% | — |
| ProofBench | — | 16% |
| MATH Level 5 | 3.7% | — |
| GSM8K | 54.4% | — |
Knowledge Qwen3.8 27B leads
Mistral 7B: 7.4 (#311), Qwen3.8 27B: 41.6 (#109)
| Benchmark | Mistral 7B | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1036 | 1482 |
| GPQA Diamond | 15.2% | — |
| ARC (AI2) Challenge | 78.6% | — |
| BoolQ | 87.4% | — |
| MMLU | 62.5% | — |
| OpenBookQA | 79.8% | — |
| TriviaQA | 75.2% | — |
Multimodal Not comparable
Mistral 7B: —, Qwen3.8 27B: 41.3 (#37)
| Benchmark | Mistral 7B | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | — | 1271 |
Multilingual Qwen3.8 27B leads
Mistral 7B: 25.8 (#283), Qwen3.8 27B: 53.7 (#60)
| Benchmark | Mistral 7B | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1012 | 1430 |
| LMArena Chinese | 1009 | 1504 |
| LMArena French | 1037 | 1465 |
| LMArena German | 987 | 1438 |
| LMArena Japanese | 878 | 1384 |
| LMArena Russian | 1018 | 1415 |
| LMArena Spanish | 1026 | 1448 |
| LMArena Korean | — | 1393 |
Instruction Following Qwen3.8 27B leads
Mistral 7B: 54.2 (#280), Qwen3.8 27B: 75.8 (#53)
| Benchmark | Mistral 7B | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1060 | 1439 |
Long Context Qwen3.8 27B leads
Mistral 7B: 32.2 (#271), Qwen3.8 27B: 44.3 (#70)
| Benchmark | Mistral 7B | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1060 | 1450 |
Writing & Preference Qwen3.8 27B leads
Mistral 7B: 30.7 (#286), Qwen3.8 27B: 65.8 (#43)
| Benchmark | Mistral 7B | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1090 | 1441 |
| LMArena Creative Writing | 1068 | 1384 |
| LMArena Multi-Turn | 1062 | 1441 |
| EQ-Bench Creative Writing | — | 1671 |
Frequently asked questions
Is Mistral 7B better than Qwen3.8 27B?
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 23.0 on the Noometry Index. Mistral 7B costs 4.5× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Which is cheaper, Mistral 7B or Qwen3.8 27B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.
Is Mistral 7B or Qwen3.8 27B better for coding?
Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 27B does, with 262K tokens against 8K.
How many benchmarks do Mistral 7B and Qwen3.8 27B share?
18 benchmarks have published results for both models. Mistral 7B has 37 scored results on Noometry and Qwen3.8 27B has 31.