Gemma 4 E2B vs Qwen3.5-9B
Gemma 4 E2B (2026) and Qwen3.5-9B (2026) are compact production models from Google DeepMind and Alibaba. Gemma 4 E2B ships a 128k-token context window, while Qwen3.5-9B ships a 262K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.
Gemma 4 E2B is safer overall; choose Qwen3.5-9B when long-context analysis matters.
Decision scorecard
Local evidence first| Signal | Gemma 4 E2B | Qwen3.5-9B |
|---|---|---|
| Decision fit | RAG, Agents, and Long context | RAG, Agents, and Long context |
| Context window | 128k | 262K |
| Cheapest output | - | $0.15/1M tokens |
| Provider routes | 1 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Gemma 4 E2B for RAG, Agents, and Long context.
- 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, Tool use, and Structured outputs 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 4 E2B
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 4 E2B and Qwen3.5-9B; plan for SDK, billing, or endpoint changes.
- Qwen3.5-9B adds Vision, Tool use, and Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Qwen3.5-9B and Gemma 4 E2B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Tool use, and Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-31 | 2026-03-02 |
| Context window | 128k | 262K |
| Parameters | 2B | 9B |
| Architecture | - | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 4 E2B | Qwen3.5-9B |
|---|---|---|
| Input price | - | $0.1/1M tokens |
| Output price | - | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 4 E2B | Qwen3.5-9B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | Yes | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | 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, tool use: Qwen3.5-9B, and structured outputs: Qwen3.5-9B. Both models share multimodal input and function calling, 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 4 E2B has no token price sourced yet and Qwen3.5-9B has $0.1/1M input tokens. Provider availability is 1 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 4 E2B 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 4 E2B or Qwen3.5-9B?
Qwen3.5-9B supports 262K tokens, while Gemma 4 E2B supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 4 E2B or Qwen3.5-9B open source?
Gemma 4 E2B 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 4 E2B 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 4 E2B or Qwen3.5-9B?
Both Gemma 4 E2B and Qwen3.5-9B expose multimodal input. 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, Gemma 4 E2B or Qwen3.5-9B?
Both Gemma 4 E2B and Qwen3.5-9B expose function calling. 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.
Where can I run Gemma 4 E2B and Qwen3.5-9B?
Gemma 4 E2B is available on 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: 2026-05-14. Data sourced from public model cards and provider documentation.