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GLM 4.7 Flash 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 GLM 4.7 Flash and Mixtral 8x22B Instruct.

Zhipu AI

GLM 4.7 Flash

As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning,...

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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

SpecificationGLM 4.7 FlashMixtral 8x22B Instruct
ProviderZhipu AIMistral
Context Window202,752 tokens65,536 tokens
Agent SuitabilityN/AN/A
Time to First Token (TTFT)N/AN/A
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-01-192024-04-17

API Pricing Comparison

Input Price per Million Tokens

GLM 4.7 Flash

$0.06

Mixtral 8x22B Instruct

$2.00

Output Price per Million Tokens

GLM 4.7 Flash

$0.40

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
7720.0%vsN/A
GLM 4.7 Flash
Mixtral 8x22B Instruct
HumanEvalPython coding & logic synthesis
7850.0%vsN/A
GLM 4.7 Flash
Mixtral 8x22B Instruct
MATHComplex mathematical problem solving
4000.0%vsN/A
GLM 4.7 Flash
Mixtral 8x22B Instruct
GPQAGraduate-level expert reasoning
3100.0%vsN/A
GLM 4.7 Flash
Mixtral 8x22B Instruct
HellaSwagCommonsense reasoning and inference
8000.0%vsN/A
GLM 4.7 Flash
Mixtral 8x22B Instruct
MT-BenchMulti-turn conversation flow quality
810.0%vsN/A
GLM 4.7 Flash
Mixtral 8x22B Instruct

GLM 4.7 Flash Quirks & Gotchas

No developer gotchas reported.

Mixtral 8x22B Instruct Quirks & Gotchas

No developer gotchas reported.