Gemma 3n vs Qwen3.5-9B
Gemma 3n (2025) and Qwen3.5-9B (2026) are compact production models from Google DeepMind and Alibaba. Gemma 3n ships a 32k-token context window, while Qwen3.5-9B ships a 262k-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.
Qwen3.5-9B fits 8x more tokens; pick it for long-context work and Gemma 3n for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 3n | Qwen3.5-9B |
|---|---|---|
| Best for | provider-routed production | multimodal apps, tool-calling agents, and provider-routed production |
| Decision fit | Classification and JSON / Tool use | RAG, Agents, and Long context |
| Context window | 32k | 262k |
| Cheapest output | - | $0.15/1M tokens |
| Provider routes | 2 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Gemma 3n for Classification and JSON / Tool use.
- Qwen3.5-9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-9B has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen3.5-9B uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Qwen3.5-9B for RAG, Agents, and 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
Qwen3.5-9B
$118
Cheapest tracked route/tier: Together AI
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 Qwen3.5-9B; plan for SDK, billing, or endpoint changes.
- Qwen3.5-9B adds Vision, Multimodal, and Function calling in local capability data.
- No overlapping tracked provider route is sourced for Qwen3.5-9B and Gemma 3n; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-12 | 2026-03-02 |
| Context window | 32k | 262k |
| Parameters | — | 9B |
| Architecture | decoder only | decoder only |
| License | Gemma | Apache 2.0 |
| Knowledge cutoff | 2024-06 | - |
Pricing and availability
| Pricing attribute | Gemma 3n | Qwen3.5-9B |
|---|---|---|
| Input price | - | $0.10/1M tokens |
| Output price | - | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 3n | Qwen3.5-9B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| 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 vision: Qwen3.5-9B, multimodal input: Qwen3.5-9B, function calling: Qwen3.5-9B, and tool use: Qwen3.5-9B. Both models share 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.
Pricing coverage is uneven: Gemma 3n has no token price sourced yet and Qwen3.5-9B has $0.10/1M input tokens. Provider availability is 2 tracked routes versus 3. 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 are central to the workload. Choose Qwen3.5-9B when long-context analysis, larger context windows, and broader provider choice 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 Qwen3.5-9B?
Qwen3.5-9B supports 262k 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Is Gemma 3n or Qwen3.5-9B open source?
Gemma 3n is listed under Gemma. Qwen3.5-9B 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 vision, Gemma 3n or Qwen3.5-9B?
Qwen3.5-9B 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Gemma 3n or Qwen3.5-9B?
Qwen3.5-9B 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.
Which is better for function calling, Gemma 3n or Qwen3.5-9B?
Qwen3.5-9B 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.
Where can I run Gemma 3n and Qwen3.5-9B?
Gemma 3n is available on Google AI Studio and GCP Vertex AI. Qwen3.5-9B is available on Together AI, OpenRouter, and Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.