# Codestral vs Gemini 2.5 Pro

> Gemini 2.5 Pro is the stronger model overall, scoring 45.0 to 30.6 on the Noometry Index. Codestral costs 7.6× less per token, which makes it the better buy when Gemini 2.5 Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/codestral-vs-gemini-2-5-pro
- Last updated: 2026-10-11
- Shared benchmarks: 3

## Summary

- They share 3 benchmarks with published results for both. Codestral scores higher in 0 categories and Gemini 2.5 Pro in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where Gemini 2.5 Pro leads 42.4 to 27.3.
- The biggest single-benchmark swing is Aider Polyglot: 11.1% for Codestral and 83.1% for Gemini 2.5 Pro.
- Codestral is cheaper at $0.30 / $0.90 per million input/output tokens, against $1.25 / $10 for Gemini 2.5 Pro.
- Gemini 2.5 Pro accepts more context: 1.05M tokens versus 256K.

## Snapshot

| | Codestral | Gemini 2.5 Pro |
|---|---|---|
| Provider | Mistral AI | Google |
| Noometry Index | 30.6 | 45.0 |
| Rank | 290 | 75 |
| Context | 256K | 1.05M |
| Input $/M | $0.30 | $1.25 |
| Output $/M | $0.90 | $10 |
| Weights | Proprietary | Proprietary |

## Coding

- Codestral: 27.3 (#321)
- Gemini 2.5 Pro: 42.4 (#101)

| Benchmark | Codestral | Gemini 2.5 Pro |
|---|---|---|
| Aider Polyglot | 11.1% | 83.1% |
| ALE-Bench | 137.78 | 785.52 |
| SWE-bench Verified | — | 57.6% |
| SWE-bench Verified (bash only) | — | 53.6% |
| LMArena WebDev | — | 1227 |
| SciCode | — | 42.8% |
| GSO | — | 3.9% |
| WeirdML | — | 54% |
| BigCodeBench Instruct | 41.8% | — |
| LiveBench Coding | — | 85.9% |
| LMArena Coding | — | 1452 |
| BigCodeBench Complete | 52.5% | — |
| CadEval | — | 64% |
| AlgoTune | — | 1.51 |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |

## Agentic & Tool Use

- Codestral: —
- Gemini 2.5 Pro: 29.2 (#88)

| Benchmark | Codestral | Gemini 2.5 Pro |
|---|---|---|
| Terminal-Bench | — | 32.6% |
| GDPval | — | 23.3% |
| Remote Labor Index | — | 0.8% |
| TheAgentCompany | — | 30.3% |
| τ²-bench Banking | — | 13.7% |
| DeepResearch Bench | — | 42.8% |
| BALROG | — | 43.3% |
| LMArena Search | — | 1142 |
| METR Time Horizons | — | 55.4% |
| Vending-Bench 2 | — | 573.64 |

## Reasoning

- Codestral: 19.8 (#251)
- Gemini 2.5 Pro: 28.8 (#99)

| Benchmark | Codestral | Gemini 2.5 Pro |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | 70.3% |
| ARC-AGI-2 | — | 4.9% |
| SimpleBench | — | 62.4% |
| ARC-AGI-1 | — | 41% |
| CritPt | — | 2% |
| Chess Puzzles | — | 20% |
| EnigmaEval | — | 5.6% |
| LiveBench Reasoning | — | 89.8% |
| LMArena Hard Prompts | — | 1455 |
| DTBench | — | 82.4% |
| LiveBench Data Analysis | — | 79.9% |
| LMCA | — | 34.8% |
| Epoch Capabilities Index | — | 145.32 |
| ForecastBench | — | 61.3 |
| LiveBench | — | 82.3% |

## Math

- Codestral: —
- Gemini 2.5 Pro: 32.5 (#213)

| Benchmark | Codestral | Gemini 2.5 Pro |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 24.6% |
| FrontierMath Tier 4 | — | 0% |
| OTIS Mock AIME 2024-2025 | — | 84.7% |
| Omni-MATH | — | 41.6% |
| LiveBench Math | — | 90.2% |
| LMArena Math | — | 1450 |
| MATH Level 5 | — | 95.9% |
| FrontierMath (Feb 2025 set) | — | 14.1% |
| FrontierMath Tier 4 (v1) | — | 4.2% |

## Knowledge

- Codestral: —
- Gemini 2.5 Pro: 56.0 (#46)

| Benchmark | Codestral | Gemini 2.5 Pro |
|---|---|---|
| GPQA Diamond | — | 85.3% |
| Humanity's Last Exam | — | 21.6% |
| MMLU-Pro | — | 86.3% |
| Confabulations | — | 10.6% |
| Vectara Hallucination Rate | — | 7% |
| GPQA (HELM) | — | 74.9% |
| LMArena Expert | — | 1452 |

## Multimodal

- Codestral: —
- Gemini 2.5 Pro: 45.2 (#18)

| Benchmark | Codestral | Gemini 2.5 Pro |
|---|---|---|
| LMArena Vision | — | 1263 |
| GeoBench | — | 86% |
| VPCT | — | 48% |
| LMArena Document | — | 1421 |
| SpatialViz-Bench | — | 44.7% |

## Multilingual

- Codestral: —
- Gemini 2.5 Pro: 55.3 (#31)

| Benchmark | Codestral | Gemini 2.5 Pro |
|---|---|---|
| LMArena Non-English | — | 1451 |
| LMArena Chinese | — | 1507 |
| LMArena French | — | 1472 |
| LMArena German | — | 1487 |
| LMArena Japanese | — | 1461 |
| LMArena Korean | — | 1434 |
| LMArena Russian | — | 1461 |
| LMArena Spanish | — | 1473 |

## Instruction Following

- Codestral: —
- Gemini 2.5 Pro: 75.0 (#75)

| Benchmark | Codestral | Gemini 2.5 Pro |
|---|---|---|
| LiveBench Instruction Following | — | 80.6% |
| IFEval | — | 84% |
| LMArena Instruction Following | — | 1437 |

## Long Context

- Codestral: —
- Gemini 2.5 Pro: 59.8 (#5)

| Benchmark | Codestral | Gemini 2.5 Pro |
|---|---|---|
| Fiction.LiveBench | — | 91.7% |
| LMArena Longer Query | — | 1449 |

## Writing & Preference

- Codestral: —
- Gemini 2.5 Pro: 63.7 (#62)

| Benchmark | Codestral | Gemini 2.5 Pro |
|---|---|---|
| LMArena Text | — | 1458 |
| LMArena Creative Writing | — | 1454 |
| Short-Story Creative Writing | — | 83.8% |
| EQ-Bench Creative Writing | — | 1421 |
| WildBench | — | 85.7% |
| LMArena Multi-Turn | — | 1453 |
| LiveBench Language | — | 67.8% |

## FAQ

### Is Codestral better than Gemini 2.5 Pro?

Gemini 2.5 Pro is the stronger model overall, scoring 45.0 to 30.6 on the Noometry Index. Codestral costs 7.6× less per token, which makes it the better buy when Gemini 2.5 Pro's lead doesn't matter for your workload.

### Which is cheaper, Codestral or Gemini 2.5 Pro?

Codestral is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Gemini 2.5 Pro lists at $1.25 and $10.

### Is Codestral or Gemini 2.5 Pro better for coding?

Gemini 2.5 Pro scores higher on coding benchmarks: 42.4 versus 27.3 in the Noometry coding category.

### Which has the bigger context window?

Gemini 2.5 Pro does, with 1.05M tokens against 256K.

### How many benchmarks do Codestral and Gemini 2.5 Pro share?

3 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and Gemini 2.5 Pro has 78.