open-mixtral-8x22b: benchmark scores, price and ranking
open-mixtral-8x22b from Mistral AI ranks #542 of 743 LLMs on the LLMs Tiger aggregate benchmark score (46.0), based on 1 benchmark results captured up to 2026-10-13.
- Organisation
- Mistral AI
- Type
- Frontier model
- Open weights
- No
- Released
- 2024-04-17 (source: epoch)
- Aggregate rank
- #542 of 743 (score 46.0)
Across the 1 benchmark where at least ten models are scored, open-mixtral-8x22b is in the top 10% on 0 and in the top half on 0.
open-mixtral-8x22b benchmark results
| Benchmark | Score | Rank | Source | Captured |
|---|---|---|---|---|
| GPQA Diamond | 34.1% | #237 of 275 | Epoch AI Benchmarking Hub | 2026-10-13 |
About open-mixtral-8x22b
open-mixtral-8x22b is a frontier model from Mistral AI. It was released on 2024-04-17, according to Epoch AI. No API price is listed for it yet. We hold 1 benchmark result for it across 1 area, and none is in the top tenth of its board.
Where open-mixtral-8x22b stands
On our aggregate score, open-mixtral-8x22b is #542 of 743 ranked models, in the lower half. Its score is 46.0; the scale is centred on 50, so it sits 4.0 points below the typical model. That beats 27% of the ranked field. Among the 44 ranked Mistral AI models it is #20. The next one up is mistral-small-2409 at 46.3. The next one down is Mixtral-8x7B-Instruct-v0.1 at 45.7.
open-mixtral-8x22b, benchmark by benchmark
GPQA Diamond is a weak spot. open-mixtral-8x22b scores 34.1%, #237 of 275, behind 86% of the models measured. The leader, GPT-6 Astra (max), reaches 95.8%. It covers expert-level science questions. The board median is 71.7%, so open-mixtral-8x22b is 37.7 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
Release history
The Mistral AI model released before it was Mixtral-8x22B-Instruct-v0.1 on 2024-04-17, which scores 43.5. The next one was Mistral-7B-Instruct-v0.3 on 2024-05-27, at 41.9.
Reading these numbers
The 1 score on this page comes from 1 source: Epoch AI Benchmarking Hub (1). No official leaderboard supplied any of them, so treat them as third-party measurements. With 1 results the aggregate rank can move a lot when one new score arrives.
Models ranked nearby
- #539 gemma2:27b-instruct-q8_0 (46.1)
- #540 Mathstral-7B-v0.1 (46.1)
- #541 MiMo-7B-Base (46.0)
- #543 SWE-1.6 (46.0)
- #544 claude-opus-4-1-20250805_32K (46.0)
- #545 Eurus-2-7B-PRIME (46.0)
More from Mistral AI
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- pixtral-large-2411 (#360, 48.3)
- Codestral-22B-v0.1-Q4_K_M (#392, 48.0)
- codestral:22b-v0.1-q8_0 (#393, 48.0)
- Mistral Large 4 (high) (#411, 47.7)
Compare it on the live leaderboard → · How the aggregate score works