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DeepSeek V4 Pro vs Mixtral 8x22B Instruct

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 DeepSeek V4 Pro and Mixtral 8x22B Instruct.

DeepSeek

DeepSeek V4 Pro

DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...

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Mistral

Mixtral 8x22B Instruct

Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b). It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include: - strong math, coding,...

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Technical Specifications

SpecificationDeepSeek V4 ProMixtral 8x22B Instruct
ProviderDeepSeekMistral
Context Window1,048,576 tokens65,536 tokens
Agent Suitability94/100N/A
Time to First Token (TTFT)280 msN/A
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-04-242024-04-17

API Pricing Comparison

Input Price per Million Tokens

DeepSeek V4 Pro

$0.43

Mixtral 8x22B Instruct

$2.00

Output Price per Million Tokens

DeepSeek V4 Pro

$0.87

Mixtral 8x22B Instruct

$6.00

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

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
8850.0%vsN/A
DeepSeek V4 Pro
Mixtral 8x22B Instruct
HumanEvalPython coding & logic synthesis
8900.0%vsN/A
DeepSeek V4 Pro
Mixtral 8x22B Instruct
MATHComplex mathematical problem solving
7460.0%vsN/A
DeepSeek V4 Pro
Mixtral 8x22B Instruct
GPQAGraduate-level expert reasoning
4900.0%vsN/A
DeepSeek V4 Pro
Mixtral 8x22B Instruct
HellaSwagCommonsense reasoning and inference
8750.0%vsN/A
DeepSeek V4 Pro
Mixtral 8x22B Instruct
MT-BenchMulti-turn conversation flow quality
918.0%vsN/A
DeepSeek V4 Pro
Mixtral 8x22B Instruct

DeepSeek V4 Pro Quirks & Gotchas

  • โ–ธMoE architecture โ€” cold-start latency on first request, use keep-alive
  • โ–ธBest cost-performance ratio of any frontier model โ€” strong tool calling for agentic use

Mixtral 8x22B Instruct Quirks & Gotchas

No developer gotchas reported.