Gemma 3n vs Magistral Small 2506
Gemma 3n (2025) and Magistral Small 2506 (2025) are frontier reasoning models from Google DeepMind and MistralAI. Gemma 3n ships a 32k-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.
Magistral Small 2506 fits 4x more tokens; pick it for long-context work and Gemma 3n for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 3n | Magistral Small 2506 |
|---|---|---|
| Best for | provider-routed production | reasoning-heavy apps |
| Decision fit | Classification and JSON / Tool use | Long context |
| Context window | 32k | 128k |
| Cheapest output | - | - |
| Provider routes | 2 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 3n has broader tracked provider coverage for fallback and procurement flexibility.
- Gemma 3n uniquely exposes Structured outputs in local model data.
- Local decision data tags Gemma 3n for Classification and JSON / Tool use.
- Magistral Small 2506 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Magistral Small 2506 uniquely exposes Reasoning in local model data.
- 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.
Gemma 3n
Unavailable
No complete token price in local provider data
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
- No overlapping tracked provider route is sourced for Gemma 3n and Magistral Small 2506; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- Magistral Small 2506 adds Reasoning in local capability data.
- No overlapping tracked provider route is sourced for Magistral Small 2506 and Gemma 3n; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning before moving production traffic.
- Gemma 3n adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-12 | 2025-06-10 |
| Context window | 32k | 128k |
| Parameters | — | 24B |
| Architecture | decoder only | decoder only |
| License | Open Weights | Proprietary |
| Knowledge cutoff | 2024-06 | 2025-06 |
Pricing and availability
| Pricing attribute | Gemma 3n | Magistral Small 2506 |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Gemma 3n | Magistral Small 2506 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | No | No |
| Tool use | No | 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 reasoning mode: Magistral Small 2506 and structured outputs: Gemma 3n. Both models share the core language-model surface, 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: Gemma 3n has no token price sourced yet and Magistral Small 2506 has no token price sourced yet. Provider availability is 2 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemma 3n when provider fit and broader provider choice are central to the workload. Choose Magistral Small 2506 when reasoning depth and larger context windows 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, Gemma 3n or Magistral Small 2506?
Magistral Small 2506 supports 128k tokens, while Gemma 3n supports 32k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 3n or Magistral Small 2506 open source?
Gemma 3n is listed under Open Weights. Magistral Small 2506 is listed under Proprietary. 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, Gemma 3n or Magistral Small 2506?
Magistral Small 2506 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 structured outputs, Gemma 3n or Magistral Small 2506?
Gemma 3n has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Gemma 3n and Magistral Small 2506?
Gemma 3n is available on Google AI Studio and GCP Vertex AI. Magistral Small 2506 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 3n over Magistral Small 2506?
Magistral Small 2506 fits 4x more tokens; pick it for long-context work and Gemma 3n for tighter calls. If your workload also depends on provider fit, start with Gemma 3n; if it depends on reasoning depth, run the same evaluation with Magistral Small 2506.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.