Mistral Large vs WizardLM 2 8x22B

Mistral Large is the better all-round choice, scoring 42/100 against WizardLM 2 8x22B (38). It leads on inputs & features and context window. WizardLM 2 8x22B wins on capability and price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond), because WizardLM 2 8x22B has no Capabilities Index score yet.

WizardLM 2 8x22B is cheaper at $0.493 input / $0.493 output per million tokens (median across 3 API providers). Mistral Large costs $0.50 input / $1.50 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.493 per million tokens for WizardLM 2 8x22B versus $0.75 for Mistral Large (1.5× as much).

Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond): WizardLM 2 8x22B 43.4% and Mistral Large 38.8%. On individual benchmarks: GPQA Diamond — WizardLM 2 8x22B 43.4%, Mistral Large 38.8%.

There are no published SWE-bench Verified results for Mistral Large and WizardLM 2 8x22B yet, so there is no like-for-like coding score. On overall capability, WizardLM 2 8x22B leads, which tends to carry over to coding, but test on your own codebase. Note that WizardLM 2 8x22B does not support tool calling, which most coding agents need.

Mistral Large has the largest context window at 128,000 tokens, against 65,536 for WizardLM 2 8x22B. Maximum output per response: Mistral Large up to 102,400, WizardLM 2 8x22B up to 8,000 tokens.

Mistral Large accepts text and PDFs; WizardLM 2 8x22B accepts text. Mistral Large handles the widest range of inputs.

WizardLM 2 8x22B publishes its weights (Apache-2.0) and can be self-hosted; Mistral Large is proprietary.

WizardLM 2 8x22B is the newest, released Apr 15, 2024. Mistral Large came out Feb 26, 2024. Knowledge cutoff: Mistral Large Nov 30, 2024.

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.