# GPT-6 Luna vs Mistral Small 3.2

> GPT-6 Luna is the stronger model overall, scoring 53.3 to 31.2 on the Noometry Index. Mistral Small 3.2 costs 1.5× less per token, which makes it the better buy when GPT-6 Luna's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-6-luna-vs-mistral-small-3-2
- Last updated: 2026-10-12
- Shared benchmarks: 4

## Summary

- They share 4 benchmarks with published results for both. GPT-6 Luna scores higher in 4 categories and Mistral Small 3.2 in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Luna leads 76.1 to 26.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.9% for GPT-6 Luna and 30.3% for Mistral Small 3.2.
- Mistral Small 3.2 is cheaper at $0.0938 / $0.25 per million input/output tokens, against $0.10 / $0.50 for GPT-6 Luna.
- GPT-6 Luna accepts more context: 1.05M tokens versus 256K.
- Mistral Small 3.2 has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-6 Luna | Mistral Small 3.2 |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 53.3 | 31.2 |
| Rank | 36 | 280 |
| Context | 1.05M | 256K |
| Input $/M | $0.10 | $0.0938 |
| Output $/M | $0.50 | $0.25 |
| Weights | Proprietary | Open |

## Coding

- GPT-6 Luna: 55.5 (#25)
- Mistral Small 3.2: —

| Benchmark | GPT-6 Luna | Mistral Small 3.2 |
|---|---|---|
| DeepSWE | 66.6% | — |
| FrontierCode | 42.4% | — |
| LMArena WebDev | 1581 | — |
| SciCode | 54.6% | — |
| LMArena Coding | 1439 | — |
| ALE-Bench | 1,577 | — |

## Agentic & Tool Use

- GPT-6 Luna: 33.3 (#54)
- Mistral Small 3.2: —

| Benchmark | GPT-6 Luna | Mistral Small 3.2 |
|---|---|---|
| APEX-Agents | 44.3% | — |
| GDP.pdf | 23% | — |

## Reasoning

- GPT-6 Luna: 48.2 (#41)
- Mistral Small 3.2: 18.1 (#287)

| Benchmark | GPT-6 Luna | Mistral Small 3.2 |
|---|---|---|
| Chess Puzzles | 31% | 1% |
| Epoch Capabilities Index | 156.28 | 131.74 |
| ARC-AGI-2 | 59.3% | — |
| Kagi LLM Benchmark | — | 40.4% |
| NYT Connections (extended) | 68.7% | — |
| ARC-AGI-1 | 86.7% | — |
| CritPt | 19.4% | — |
| LMArena Hard Prompts | 1411 | — |
| Mystery Game Puzzles | 7% | — |
| DTBench | 90.1% | — |
| LMCA | 44.5% | — |

## Math

- GPT-6 Luna: 76.1 (#15)
- Mistral Small 3.2: 26.3 (#260)

| Benchmark | GPT-6 Luna | Mistral Small 3.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.9% | 30.3% |
| FrontierMath (Tiers 1-3) | 78.9% | — |
| FrontierMath Tier 4 | 56.1% | — |
| ProofBench | 64% | — |
| LMArena Math | 1416 | — |

## Knowledge

- GPT-6 Luna: 57.0 (#41)
- Mistral Small 3.2: 26.7 (#256)

| Benchmark | GPT-6 Luna | Mistral Small 3.2 |
|---|---|---|
| GPQA Diamond | 90.5% | 49.1% |
| SimpleQA Verified | 41.4% | — |
| LMArena Expert | 1444 | — |

## Multimodal

- GPT-6 Luna: 42.4 (#30)
- Mistral Small 3.2: —

| Benchmark | GPT-6 Luna | Mistral Small 3.2 |
|---|---|---|
| LMArena Vision | 1217 | — |
| Blueprint-Bench 2 | 31.2% | — |
| Furniture Assembly | 44.2% | — |

## Multilingual

- GPT-6 Luna: 50.5 (#117)
- Mistral Small 3.2: —

| Benchmark | GPT-6 Luna | Mistral Small 3.2 |
|---|---|---|
| LMArena Non-English | 1386 | — |
| LMArena Chinese | 1433 | — |
| LMArena French | 1420 | — |
| LMArena German | 1369 | — |
| LMArena Japanese | 1369 | — |
| LMArena Korean | 1360 | — |
| LMArena Russian | 1394 | — |
| LMArena Spanish | 1393 | — |

## Instruction Following

- GPT-6 Luna: 74.3 (#99)
- Mistral Small 3.2: —

| Benchmark | GPT-6 Luna | Mistral Small 3.2 |
|---|---|---|
| LMArena Instruction Following | 1409 | — |

## Long Context

- GPT-6 Luna: 43.0 (#111)
- Mistral Small 3.2: —

| Benchmark | GPT-6 Luna | Mistral Small 3.2 |
|---|---|---|
| LMArena Longer Query | 1409 | — |

## Writing & Preference

- GPT-6 Luna: 58.3 (#119)
- Mistral Small 3.2: 45.0 (#224)

| Benchmark | GPT-6 Luna | Mistral Small 3.2 |
|---|---|---|
| LMArena Text | 1391 | — |
| LMArena Creative Writing | 1363 | — |
| EQ-Bench Creative Writing | — | 1255 |
| LMArena Multi-Turn | 1396 | — |

## FAQ

### Is GPT-6 Luna better than Mistral Small 3.2?

GPT-6 Luna is the stronger model overall, scoring 53.3 to 31.2 on the Noometry Index. Mistral Small 3.2 costs 1.5× less per token, which makes it the better buy when GPT-6 Luna's lead doesn't matter for your workload.

### Which is cheaper, GPT-6 Luna or Mistral Small 3.2?

Mistral Small 3.2 is cheaper. It lists at $0.0938 per million input tokens and $0.25 per million output tokens; GPT-6 Luna lists at $0.10 and $0.50.

### Which has the bigger context window?

GPT-6 Luna does, with 1.05M tokens against 256K.

### How many benchmarks do GPT-6 Luna and Mistral Small 3.2 share?

4 benchmarks have published results for both models. GPT-6 Luna has 42 scored results on Noometry and Mistral Small 3.2 has 6.