Claude Opus 4.7 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 4.7 and Mixtral 8x22B.
Claude Opus 4.7
Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on...
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.
Technical Specifications
| Specification | Claude Opus 4.7 | Mixtral 8x22B |
|---|---|---|
| Provider | Anthropic | Mistral |
| Context Window | 1,000,000 tokens | 65,536 tokens |
| Agent Suitability | 96/100 | 87/100 |
| Time to First Token (TTFT) | 480 ms | 320 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-16 | 2024-12-11 |
API Pricing Comparison
Input Price per Million Tokens
Claude Opus 4.7
$5.00
Mixtral 8x22B
$0.50
Output Price per Million Tokens
Claude Opus 4.7
$25.00
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.
Claude Opus 4.7 Quirks & Gotchas
- ▸Top-tier agentic coding model — excels at autonomous software engineering
- ▸Requires explicit tool_choice parameter for parallel function calling to work reliably
Mixtral 8x22B Quirks & Gotchas
- ▸MoE architecture — efficient inference for its capability tier
- ▸Requires ~90GB VRAM at FP16 — 4-bit quantization recommended for single-GPU deployment