Mixtral 8x22B
Not tested yet
Language modelOpen source
Mixtral 8x22B
Mistral AI
- Released
- April 2024
- Data date
- September 8, 2026
- Intelligence
- Overall score
- Price
- Input · Output / 1M tokens
- Input
- Text
- Output
- Text
No published category-wide ranking is available for this model.
Entries 1-12 of 26. Page 1 of 3.
Measurements without matching peer values
These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.
GSM8K maj@8 (Show measurement, test conditions, and source)
- Source value
- 90.8
- Score
- 90.8
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:0
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; maj@8, as stated in release post
Math maj@4 (Show measurement, test conditions, and source)
- Source value
- 44.6
- Score
- 44.6
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:1
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; maj@4, as stated in release post
MMLU (Show measurement, test conditions, and source)
- Source value
- 77.75
- Score
- 77.75
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:2
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; Figure 1 benchmark row
HellaSwag (Show measurement, test conditions, and source)
- Source value
- 88.5
- Score
- 88.5
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:3
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; Figure 1 benchmark row
WinoGrande (Show measurement, test conditions, and source)
- Source value
- 84.7
- Score
- 84.7
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:4
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; Figure 1 benchmark row
ARC-C5 (Show measurement, test conditions, and source)
- Source value
- 91.3
- Score
- 91.3
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:5
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; Figure 1 benchmark row
ARC-C25 (Show measurement, test conditions, and source)
- Source value
- 91.3
- Score
- 91.3
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:6
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; Figure 1 benchmark row
TriviaQA (Show measurement, test conditions, and source)
- Source value
- 82.2
- Score
- 82.2
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:7
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; Figure 1 benchmark row
Natural Questions (Show measurement, test conditions, and source)
- Source value
- 40.1
- Score
- 40.1
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:8
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; Figure 1 benchmark row
ARC-C (French) (Show measurement, test conditions, and source)
- Source value
- 59.1
- Score
- 59.1
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:9
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; Figure 2 French row
HellaSwag (French) (Show measurement, test conditions, and source)
- Source value
- 79.2
- Score
- 79.2
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:10
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; Figure 2 French row
MMLU (French) (Show measurement, test conditions, and source)
- Source value
- 75.1
- Score
- 75.1
- Metric
- percent
- Unit
- %
- Direction
- Higher is better
- Comparison cohort
- research:mixtral-8x22b:11
- Source type
- vendor-reported
- Evaluator
- Mistral AI release post
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. Instruct model; Figure 2 French row
Our model tests
Community tests you can inspect
Compare how the models respond to the same prompt. Each test shows the first attempt, with no subsequent fixes to the generated code. These results do not contribute to an overall score.
Pelican on a bicycle
A community classic for free-form SVG drawing.
Prompt and test conditions
Generate an SVG of a pelican riding a bicycle
Token limit including reasoning: 8,192 tokens
Task origin (Simon Willison)Profile
Specifications and access
Published information about this model. Existing estimates are explicitly labeled.
Additional source observations
Dated documentation and model-card observations. Provider limits, native context and extended context can differ. Configuration notes retain the source wording.
- License
- Apache-2.0Source
- Release
- 2024-04-17Source
- Parameters
- 141 BSource
- Active parameters
- 39 BSource
- Architecture
- sparse mixture-of-expertsSource
- Context window
- 65,536 tokensSource
- Input
- TextSource
- Output
- TextSource
- Languages
- English, French, Italian, German, SpanishSource
- Tool calling
- YesSource
- Structured output
- YesSource
Pricing
Published prices
Prices apply to the stated unit. Resolution, output length, and provider can change the cost.
Cost example
100 API requests
$0.5
100 requests with 1,000 input and 500 output tokens each calculate to $0.5. Input accounts for $0.2; output accounts for $0.3.
- Input price
- $2 / 1M tokensBase tier for each request
- Output price
- $6 / 1M tokensBase tier for each request
The calculation uses documented token prices and no cache discount. It excludes extra tools, tax, reasoning tokens, and further hidden output tokens.
Open sourceHead-to-head comparisons
Compare this model
Each matchup compares this model with exactly one other model from the same category.
There are no published direct comparisons for this model yet.
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Evidence
Sources and data date
Every statement links to its underlying documentation or leaderboard.
- Price
Base API prices without caching or batch discounts. Higher context tiers and other rates are listed under Costs.
- Input
Documented modalities. Retrieved September 8, 2026.
- Output
Documented modalities. Retrieved September 8, 2026.
Research date September 8, 2026. 3 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Open evidence questions and research notes (original wording)
Not found in the inspected sources
knowledge-cutoff. Inventory has February 2024, but checked official card/release does not publish an exact cutoff.
Not found in the inspected sources
training-tokens. No token count found.
Not found in the inspected sources
max-output-tokens. No model-specific maximum output found.
Unresolved
AA benchmark rows. No exact Artificial Analysis model page located in the checked public sitemap.