GLM-5 vs Mistral Large 2
GLM-5 (2026) and Mistral Large 2 (2025) are frontier reasoning models from Zhipu AI and MistralAI. GLM-5 ships a 200k-token context window, while Mistral Large 2 ships a 128K-token context window. On pricing, Mistral Large 2 costs $0.48/1M input tokens versus $0.72/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Mistral Large 2 is ~50% cheaper at $0.48/1M; pay for GLM-5 only for reasoning depth.
Specs
| Released | 2026-02-11 | 2025-11-25 |
| Context window | 200k | 128K |
| Parameters | 744B total, 40B active | 123B |
| Architecture | mixture of experts | decoder only |
| License | MIT | True |
| Knowledge cutoff | - | 2025-07 |
Pricing and availability
| GLM-5 | Mistral Large 2 | |
|---|---|---|
| Input price | $0.72/1M tokens | $0.48/1M tokens |
| Output price | $2.3/1M tokens | $2.4/1M tokens |
| Providers |
Capabilities
| GLM-5 | Mistral Large 2 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Mistral Large 2, multimodal input: Mistral Large 2, and reasoning mode: GLM-5. Both models share function calling, tool use, and structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, GLM-5 lists $0.72/1M input and $2.3/1M output tokens, while Mistral Large 2 lists $0.48/1M input and $2.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mistral Large 2 lower by about $0.14 per million blended tokens. Availability is 5 providers versus 4, so concentration risk also matters.
Choose GLM-5 when reasoning depth, larger context windows, and broader provider choice are central to the workload. Choose Mistral Large 2 when vision-heavy evaluation and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, GLM-5 or Mistral Large 2?
GLM-5 supports 200k tokens, while Mistral Large 2 supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, GLM-5 or Mistral Large 2?
Mistral Large 2 is cheaper on tracked token pricing. GLM-5 costs $0.72/1M input and $2.3/1M output tokens. Mistral Large 2 costs $0.48/1M input and $2.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5 or Mistral Large 2 open source?
GLM-5 is listed under MIT. Mistral Large 2 is listed under True. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, GLM-5 or Mistral Large 2?
Mistral Large 2 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, GLM-5 or Mistral Large 2?
Mistral Large 2 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run GLM-5 and Mistral Large 2?
GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. Mistral Large 2 is available on OpenRouter, IBM watsonx, AWS Bedrock, and Mistral AI Studio. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.