GLM-5 vs Magistral Small 2506
GLM-5 (2026) and Magistral Small 2506 (2025) are frontier-tier reasoning models from Zhipu AI and MistralAI. GLM-5 ships a 200k-token context window, while Magistral Small 2506 ships a 128k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.
GLM-5 is safer overall; choose Magistral Small 2506 when provider fit matters.
Decision scorecard
Local evidence first| Signal | GLM-5 | Magistral Small 2506 |
|---|---|---|
| Best for | reasoning-heavy apps, tool-calling agents, and provider-routed production | reasoning-heavy apps |
| Decision fit | Coding, RAG, and Agents | Long context |
| Context window | 200k | 128k |
| Cheapest output | $2.08/1M tokens | - |
| Provider routes | 7 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- 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 procurement flexibility.
- GLM-5 uniquely exposes Function calling, Tool use, and Structured outputs in local model data.
- Local decision data tags GLM-5 for Coding, RAG, and Agents.
- Local decision data tags Magistral Small 2506 for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GLM-5
$1,000
Cheapest tracked route/tier: OpenRouter
Magistral Small 2506
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Function calling, Tool use, and Structured outputs before moving production traffic.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- GLM-5 adds Function calling, Tool use, and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-11 | 2025-06-10 |
| Context window | 200k | 128k |
| Parameters | 744B total, 40B active | 24B |
| Architecture | mixture of experts | decoder only |
| License | MIT(OSI) | Apache 2.0(OSI) |
| Openness | Open source | Open source |
| Commercial use | Commercial use allowed | Commercial use allowed |
| Knowledge cutoff | 2025-11 | 2025-06 |
Pricing and availability
| Pricing attribute | GLM-5 | Magistral Small 2506 |
|---|---|---|
| Input price | $0.60/1M tokens | - |
| Output price | $2.08/1M tokens | - |
| Providers |
Capabilities
| Capability | GLM-5 | Magistral Small 2506 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | Yes |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on function calling: GLM-5, tool use: GLM-5, and structured outputs: GLM-5. Both models share reasoning mode, 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.
Pricing coverage is uneven: GLM-5 has $0.60/1M input tokens and Magistral Small 2506 has no token price sourced yet. Provider availability is 7 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GLM-5 when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose Magistral Small 2506 when provider fit 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, GLM-5 or Magistral Small 2506?
GLM-5 supports 200k tokens, while Magistral Small 2506 supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is GLM-5 or Magistral Small 2506 open source?
GLM-5 is listed under MIT. Magistral Small 2506 is listed under Apache 2.0. 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 Magistral Small 2506?
Both GLM-5 and Magistral Small 2506 expose reasoning mode. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for function calling, GLM-5 or Magistral Small 2506?
GLM-5 has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, GLM-5 or Magistral Small 2506?
GLM-5 has the clearer documented tool use signal in this comparison. If tool use 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 Magistral Small 2506?
GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. Magistral Small 2506 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.