# DeepSeek-V3.1-Terminus vs Mistral Small

> DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 33.1 on the Noometry Index. Mistral Small costs 1.8× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-terminus-vs-mistral-small
- Last updated: 2026-10-10
- Shared benchmarks: 16

## Summary

- They share 16 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and Mistral Small in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.1-Terminus leads 35.7 to 15.4.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 57.4% for DeepSeek-V3.1-Terminus and 37.8% for Mistral Small.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.30 / $1 for DeepSeek-V3.1-Terminus.
- Mistral Small accepts more context: 256K tokens versus 164K.

## Snapshot

| | DeepSeek-V3.1-Terminus | Mistral Small |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.6 | 33.1 |
| Rank | 96 | 238 |
| Context | 164K | 256K |
| Input $/M | $0.30 | $0.15 |
| Output $/M | $1 | $0.60 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1-Terminus: 41.5 (#114)
- Mistral Small: 33.5 (#242)

| Benchmark | DeepSeek-V3.1-Terminus | Mistral Small |
|---|---|---|
| SciCode | 40.6% | 26.5% |
| LMArena Coding | 1426 | 1363 |
| ALE-Bench | 745.17 | 497.62 |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |

## Agentic & Tool Use

- DeepSeek-V3.1-Terminus: —
- Mistral Small: 27.9 (#87)

| Benchmark | DeepSeek-V3.1-Terminus | Mistral Small |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.1% |

## Reasoning

- DeepSeek-V3.1-Terminus: 25.7 (#126)
- Mistral Small: 19.3 (#238)

| Benchmark | DeepSeek-V3.1-Terminus | Mistral Small |
|---|---|---|
| Kagi LLM Benchmark | 57.4% | 37.8% |
| CritPt | 1.7% | 0% |
| LMArena Hard Prompts | 1427 | 1336 |
| DTBench | 81.3% | 70.9% |
| LMCA | 28.6% | 20.6% |
| LiveBench Reasoning | — | 44.8% |
| LiveBench Data Analysis | — | 53.7% |
| LiveBench | — | 44% |

## Math

- DeepSeek-V3.1-Terminus: 35.7 (#138)
- Mistral Small: 15.4 (#287)

| Benchmark | DeepSeek-V3.1-Terminus | Mistral Small |
|---|---|---|
| LMArena Math | 1402 | 1341 |
| OTIS Mock AIME 2024-2025 | — | 5.8% |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |

## Knowledge

- DeepSeek-V3.1-Terminus: —
- Mistral Small: 30.9 (#214)

| Benchmark | DeepSeek-V3.1-Terminus | Mistral Small |
|---|---|---|
| GPQA Diamond | — | 47.5% |
| Vectara Hallucination Rate | — | 5.1% |
| LMArena Expert | — | 1293 |
| MMLU | — | 68.7% |

## Multimodal

- DeepSeek-V3.1-Terminus: —
- Mistral Small: 32.9 (#91)

| Benchmark | DeepSeek-V3.1-Terminus | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |

## Multilingual

- DeepSeek-V3.1-Terminus: 52.3 (#87)
- Mistral Small: 45.7 (#164)

| Benchmark | DeepSeek-V3.1-Terminus | Mistral Small |
|---|---|---|
| LMArena Non-English | 1407 | 1315 |
| LMArena Russian | 1434 | 1324 |
| LMArena Chinese | — | 1342 |
| LMArena French | — | 1337 |
| LMArena German | — | 1341 |
| LMArena Japanese | — | 1275 |
| LMArena Korean | — | 1260 |
| LMArena Spanish | — | 1346 |

## Instruction Following

- DeepSeek-V3.1-Terminus: 74.4 (#96)
- Mistral Small: 66.6 (#204)

| Benchmark | DeepSeek-V3.1-Terminus | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1406 | 1310 |
| LiveBench Instruction Following | — | 63.7% |

## Long Context

- DeepSeek-V3.1-Terminus: 43.5 (#88)
- Mistral Small: 40.4 (#151)

| Benchmark | DeepSeek-V3.1-Terminus | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1426 | 1327 |

## Writing & Preference

- DeepSeek-V3.1-Terminus: 61.3 (#87)
- Mistral Small: 52.6 (#165)

| Benchmark | DeepSeek-V3.1-Terminus | Mistral Small |
|---|---|---|
| LMArena Text | 1420 | 1338 |
| LMArena Creative Writing | 1404 | 1304 |
| LMArena Multi-Turn | 1411 | 1344 |
| LiveBench Language | — | 30.5% |

## FAQ

### Is DeepSeek-V3.1-Terminus better than Mistral Small?

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 33.1 on the Noometry Index. Mistral Small costs 1.8× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.1-Terminus or Mistral Small?

Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; DeepSeek-V3.1-Terminus lists at $0.30 and $1.

### Is DeepSeek-V3.1-Terminus or Mistral Small better for coding?

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 41.5 versus 33.5 in the Noometry coding category.

### Which has the bigger context window?

Mistral Small does, with 256K tokens against 164K.

### How many benchmarks do DeepSeek-V3.1-Terminus and Mistral Small share?

16 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Mistral Small has 39.