Mixtral 8x22B Instruct vs Mixtral 8x7B Instruct v0.1
Too close to call on our weighted score (Mixtral 8x7B Instruct v0.1 34, Mixtral 8x22B Instruct 34). The right pick depends on what you value most.
Mistral AIMixtral 8x22B Instruct
34/100- ECI122.0
- Price$2.00 / $6.00
- Context66K
- Mistral AI
Mixtral 8x7B Instruct v0.1
34/100- ECI118.4
- Price$0.444 / $0.579
- Context33K
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within a point (Mixtral 8x7B Instruct v0.1 34/100, Mixtral 8x22B Instruct 34/100), so choose by what matters most for your work: Mixtral 8x22B Instruct for raw capability and Mixtral 8x7B Instruct v0.1 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMixtral 8x22B InstructCapabilities Index (ECI): Mixtral 8x22B Instruct 122.0 · Mixtral 8x7B Instruct v0.1 118.4
- Lowest priceMixtral 8x7B Instruct v0.1Mixtral 8x7B Instruct v0.1 $0.478 · Mixtral 8x22B Instruct $3.00 per 1M tokens (3:1 blend)
- Longest contextMixtral 8x22B InstructMixtral 8x22B Instruct 65,536 · Mixtral 8x7B Instruct v0.1 32,768 tokens
- Widest inputsMixtral 8x22B InstructMixtral 8x22B Instruct: Text, PDFs · Mixtral 8x7B Instruct v0.1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mixtral 8x22B Instruct | Mixtral 8x7B Instruct v0.1 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 42 | 38 |
| Price | 25% | 26 | 62 |
| Inputs & features | 15% | 35 | 0 |
| Context window | 10% | 12 | 0 |
| Overall | 100% | 34/100 | 34/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mixtral 8x22B InstructMistral AI | Mixtral 8x7B Instruct v0.1Mistral AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 122.0 (best) | 118.4 |
| ECI rank | #155 of 165 (best) | #160 of 165 |
| GPQA DiamondGraduate-level science questions | 34.1% (best) | 30.6% |
| Price per million tokens | ||
| Input | $2.00 | $0.444 (best) |
| Output | $6.00 | $0.579 (best) |
| Cached input | — | — |
| Blended (3:1) | $3.00 | $0.478 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 5 providers | Median of 2 providers |
| Limits | ||
| Context window | 65,536 tokens (best) | 32,768 tokens |
| Max output | 52,428 tokens (best) | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | Yes | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | No |
| Structured output | No | No |
| Availability | ||
| Weights | Open | OpenApache 2.0 |
| API model ID | — | — |
| API providers | 5 (best) | 2 |
| Released | Apr 17, 2024 | Dec 11, 2023 |
| Knowledge cutoff | Jan 31, 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mixtral 8x22B Instruct$32.00
- Mixtral 8x7B Instruct v0.1$5.60
Which should you choose?
Which is better: Mixtral 8x22B Instruct or Mixtral 8x7B Instruct v0.1?
It is close. Our weighted score puts them within a point (Mixtral 8x7B Instruct v0.1 34/100, Mixtral 8x22B Instruct 34/100), so choose by what matters most for your work: Mixtral 8x22B Instruct for raw capability and Mixtral 8x7B Instruct v0.1 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Mixtral 8x22B Instruct or Mixtral 8x7B Instruct v0.1?
Mixtral 8x7B Instruct v0.1 is cheaper at $0.444 input / $0.579 output per million tokens (median across 2 API providers). Mixtral 8x22B Instruct costs $2.00 input / $6.00 output per million tokens (median across 5 API providers). At a typical mix of three input tokens to one output token, that is $0.478 per million tokens for Mixtral 8x7B Instruct v0.1 versus $3.00 for Mixtral 8x22B Instruct (6.3× as much).
Which scores higher on benchmarks?
Mixtral 8x22B Instruct scores higher on the Capabilities Index (ECI): Mixtral 8x22B Instruct 122.0 (#155 of 165) and Mixtral 8x7B Instruct v0.1 118.4 (#160 of 165). The confidence ranges of the top two overlap (115.7–124.3 vs 110.9–121.0), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mixtral 8x22B Instruct 34.1%, Mixtral 8x7B Instruct v0.1 30.6%.
Which is better for coding?
There are no published SWE-bench Verified results for Mixtral 8x22B Instruct and Mixtral 8x7B Instruct v0.1 yet, so there is no like-for-like coding score. On overall capability, Mixtral 8x22B Instruct leads, which tends to carry over to coding, but test on your own codebase. Note that Mixtral 8x7B Instruct v0.1 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Mixtral 8x22B Instruct has the largest context window at 65,536 tokens, against 32,768 for Mixtral 8x7B Instruct v0.1. Maximum output per response: Mixtral 8x22B Instruct up to 52,428, Mixtral 8x7B Instruct v0.1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Mixtral 8x22B Instruct accepts text and PDFs; Mixtral 8x7B Instruct v0.1 accepts text. Mixtral 8x22B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Mixtral 8x22B Instruct is the newest, released Apr 17, 2024. Mixtral 8x7B Instruct v0.1 came out Dec 11, 2023. Knowledge cutoff: Mixtral 8x22B Instruct Jan 31, 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
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