Gemma 2B vs Qwen3.5-9B
Gemma 2B (2024) and Qwen3.5-9B (2026) are compact production models from Google DeepMind and Alibaba. Gemma 2B ships a 2K-token context window, while Qwen3.5-9B ships a 262K-token context window. On Google-Proof Q&A, Qwen3.5-9B leads by 41.9 pts. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3.5-9B fits 131x more tokens; pick it for long-context work and Gemma 2B for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 2B | Qwen3.5-9B |
|---|---|---|
| Decision fit | Coding and Classification | RAG, Agents, and Long context |
| Context window | 2K | 262K |
| Cheapest output | - | $0.15/1M tokens |
| Provider routes | 0 tracked | 3 tracked |
| Shared benchmarks | 1 rows | Google-Proof Q&A leader |
Decision tradeoffs
- Local decision data tags Gemma 2B for Coding and Classification.
- Qwen3.5-9B leads the largest shared benchmark signal on Google-Proof Q&A by 41.9 points.
- 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 prices on this page.
Gemma 2B
Unavailable
No complete token price in local provider data
Qwen3.5-9B
$118
Cheapest tracked route: 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 2B 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 2B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-21 | 2026-03-02 |
| Context window | 2K | 262K |
| Parameters | 2B | 9B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2023-04 | - |
Pricing and availability
| Pricing attribute | Gemma 2B | Qwen3.5-9B |
|---|---|---|
| Input price | - | $0.1/1M tokens |
| Output price | - | $0.15/1M tokens |
| Providers | - |
Capabilities
| Capability | Gemma 2B | Qwen3.5-9B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Gemma 2B | Qwen3.5-9B |
|---|---|---|
| Google-Proof Q&A | 39.8 | 81.7 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Gemma 2B at 39.8 and Qwen3.5-9B at 81.7, with Qwen3.5-9B ahead by 41.9 points. The largest visible gap is 41.9 points on Google-Proof Q&A, 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 vision: Qwen3.5-9B, multimodal input: Qwen3.5-9B, function calling: Qwen3.5-9B, tool use: Qwen3.5-9B, and structured outputs: Qwen3.5-9B. 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 2B has no token price sourced yet and Qwen3.5-9B has $0.1/1M input tokens. Provider availability is 0 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 2B 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.
FAQ
Which has a larger context window, Gemma 2B or Qwen3.5-9B?
Qwen3.5-9B supports 262K tokens, while Gemma 2B supports 2K 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 2B or Qwen3.5-9B open source?
Gemma 2B is listed under Open Source. 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 2B 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 2B 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 2B 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 2B and Qwen3.5-9B?
Gemma 2B is available on the tracked providers still being sourced. 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: 2026-05-14. Data sourced from public model cards and provider documentation.