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Claude Opus 4 vs Mixtral 8x22B

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Claude Opus 4 and Mixtral 8x22B.

Anthropic

Claude Opus 4

Claude Opus 4 is benchmarked as the world’s best coding model, at time of release, bringing sustained performance on complex, long-running tasks and agent workflows. It sets new benchmarks in...

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Mistral

Mixtral 8x22B

Mixtral 8x22B is Mistral AI's open-weight Mixture-of-Experts model, activating only 39B of its 141B total parameters per token to deliver frontier-level performance at inference costs comparable to a much smaller dense model. Released under the Apache 2.0 license, Mixtral 8x22B is one of the most capable fully open-weight models available, with strong multilingual performance, robust coding ability, and efficient fine-tuning via LoRA. It is widely deployed across self-hosted infrastructure, including Ollama, vLLM, and Hugging Face TGI.

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Technical Specifications

SpecificationClaude Opus 4Mixtral 8x22B
ProviderAnthropicMistral
Context Window200,000 tokens65,536 tokens
Agent SuitabilityN/A87/100
Time to First Token (TTFT)N/A320 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-05-222024-12-11

API Pricing Comparison

Input Price per Million Tokens

Claude Opus 4

$15.00

Mixtral 8x22B

$0.50

Output Price per Million Tokens

Claude Opus 4

$75.00

Mixtral 8x22B

$1.00

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Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
9050.0%vsN/A
Claude Opus 4
Mixtral 8x22B
HumanEvalPython coding & logic synthesis
9300.0%vsN/A
Claude Opus 4
Mixtral 8x22B
MATHComplex mathematical problem solving
8540.0%vsN/A
Claude Opus 4
Mixtral 8x22B
GPQAGraduate-level expert reasoning
7230.0%vsN/A
Claude Opus 4
Mixtral 8x22B
HellaSwagCommonsense reasoning and inference
9650.0%vsN/A
Claude Opus 4
Mixtral 8x22B
MT-BenchMulti-turn conversation flow quality
945.0%vsN/A
Claude Opus 4
Mixtral 8x22B

Claude Opus 4 Quirks & Gotchas

No developer gotchas reported.

Mixtral 8x22B Quirks & Gotchas

  • β–ΈMoE architecture β€” efficient inference for its capability tier
  • β–ΈRequires ~90GB VRAM at FP16 β€” 4-bit quantization recommended for single-GPU deployment