GLM-5 vs Mistral Large 2 (2407)
GLM-5 (2026) and Mistral Large 2 (2407) (2024) are frontier reasoning models from Zhipu AI and MistralAI. GLM-5 ships a 200K-token context window, while Mistral Large 2 (2407) ships a 128K-token context window. On MMLU PRO, GLM-5 leads by 20.1 pts. On pricing, Mistral Large 2 (2407) costs $0.50/1M input tokens versus $1/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Mistral Large 2 (2407) is ~100% cheaper at $0.50/1M; pay for GLM-5 only for reasoning depth.
Decision scorecard
Local evidence first| Signal | GLM-5 | Mistral Large 2 (2407) |
|---|---|---|
| Best for | reasoning-heavy apps, tool-calling agents, and provider-routed production | tool-calling agents and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 200K | 128K |
| Cheapest output | $3.20/1M tokens | $1.50/1M tokens |
| Provider routes | 8 tracked | 7 tracked |
| Shared benchmarks | MMLU PRO leader | 1 shared |
Decision tradeoffs
- GLM-5 holds a shared-benchmark lead on MMLU PRO, ahead by 20.1 points.
- GLM-5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5 has broader tracked provider coverage for fallback and route flexibility.
- GLM-5 uniquely exposes Reasoning in local model data.
- Local decision data tags GLM-5 for Coding, RAG, and Agents.
- Mistral Large 2 (2407) has the lower cheapest tracked output price at $1.50/1M tokens.
- Local decision data tags Mistral Large 2 (2407) for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GLM-5
$1,600
Cheapest tracked route/tier: Fireworks AI
Mistral Large 2 (2407)
$775
Cheapest tracked route/tier: Chutes AI
Estimated monthly gap: $825. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Mistral Large 2 (2407) is $1.70/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning before moving production traffic.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- GLM-5 is $1.70/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GLM-5 adds Reasoning in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-11 | 2024-07-24 |
| Context window | 200K | 128K |
| Parameters | 744B total, 40B active | 123B |
| Architecture | Mixture of Experts | Decoder Only |
| License | MITOSI-approved | Mistral License |
| Openness | Open source | Open weights |
| Weights | Available | Available |
| Code | Unknown | Unknown |
| Commercial use | Permitted | Not permitted |
| Knowledge cutoff | 2025-11 | 2024-03 |
Pricing and availability
| Pricing attribute | GLM-5 | Mistral Large 2 (2407) |
|---|---|---|
| Input price | $1/1M tokens | $0.50/1M tokens |
| Output price | $3.20/1M tokens | $1.50/1M tokens |
| Providers |
Capabilities
| Capability | GLM-5 | Mistral Large 2 (2407) |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| JSON / Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | GLM-5 | Mistral Large 2 (2407) |
|---|---|---|
| MMLU PRO | 86.0 | 65.9 |
Deep dive
On shared benchmark coverage, MMLU PRO has GLM-5 at 86 and Mistral Large 2 (2407) at 65.9, with GLM-5 ahead by 20.1 points. The largest visible gap is 20.1 points on MMLU PRO, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on reasoning mode: GLM-5 and JSON / Tool use: GLM-5. Both models share JSON / 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 $1/1M input and $3.20/1M output tokens on the cheapest tracked provider, while Mistral Large 2 (2407) lists $0.50/1M input and $1.50/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mistral Large 2 (2407) lower by about $0.86 per million blended tokens. Availability is 8 providers versus 7, 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 (2407) when provider fit 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.
FAQ
Which has a larger context window, GLM-5 or Mistral Large 2 (2407)?
GLM-5 supports 200K tokens, while Mistral Large 2 (2407) 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 (2407)?
Mistral Large 2 (2407) is cheaper on tracked token pricing. GLM-5 costs $1/1M input and $3.20/1M output tokens. Mistral Large 2 (2407) costs $0.50/1M input and $1.50/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5 or Mistral Large 2 (2407) open source?
GLM-5 is listed under MIT. Mistral Large 2 (2407) is listed under Mistral License. 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 reasoning mode, GLM-5 or Mistral Large 2 (2407)?
GLM-5 has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for JSON / Tool use, GLM-5 or Mistral Large 2 (2407)?
Both GLM-5 and Mistral Large 2 (2407) expose JSON / Tool use. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run GLM-5 and Mistral Large 2 (2407)?
GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. Mistral Large 2 (2407) is available on Microsoft Foundry, IBM watsonx, OpenRouter, AWS Bedrock, and Chutes AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-10-17. Data sourced from public model cards and provider documentation.