Gemma 3n vs Together AI Qwen2-7B-Instruct
Gemma 3n (2025) and Together AI Qwen2-7B-Instruct (2024) are compact production models from Google DeepMind and Alibaba. Gemma 3n ships a 32K-token context window, while Together AI Qwen2-7B-Instruct ships a 33K-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 3n is safer overall; choose Together AI Qwen2-7B-Instruct when long-context analysis matters.
Decision scorecard
Local evidence first| Signal | Gemma 3n | Together AI Qwen2-7B-Instruct |
|---|---|---|
| Decision fit | Classification and JSON / Tool use | Classification and JSON / Tool use |
| Context window | 32K | 33K |
| Cheapest output | - | $0.15/1M tokens |
| 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.
- Local decision data tags Gemma 3n for Classification and JSON / Tool use.
- Together AI Qwen2-7B-Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Together AI Qwen2-7B-Instruct for Classification and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Gemma 3n
Unavailable
No complete token price in local provider data
Together AI Qwen2-7B-Instruct
$158
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 3n and Together AI Qwen2-7B-Instruct; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Together AI Qwen2-7B-Instruct and Gemma 3n; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-12 | 2024-06-07 |
| Context window | 32K | 33K |
| Parameters | — | 7B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | 2024-06 | - |
Pricing and availability
| Pricing attribute | Gemma 3n | Together AI Qwen2-7B-Instruct |
|---|---|---|
| Input price | - | $0.15/1M tokens |
| Output price | - | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 3n | Together AI Qwen2-7B-Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint is close: both models cover structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
Pricing coverage is uneven: Gemma 3n has no token price sourced yet and Together AI Qwen2-7B-Instruct has $0.15/1M input tokens. 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 Together AI Qwen2-7B-Instruct when long-context analysis 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 Together AI Qwen2-7B-Instruct?
Together AI Qwen2-7B-Instruct supports 33K 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 Together AI Qwen2-7B-Instruct open source?
Gemma 3n is listed under Open Source. Together AI Qwen2-7B-Instruct is listed under Open Source. 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 structured outputs, Gemma 3n or Together AI Qwen2-7B-Instruct?
Both Gemma 3n and Together AI Qwen2-7B-Instruct expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Gemma 3n and Together AI Qwen2-7B-Instruct?
Gemma 3n is available on Google AI Studio and GCP Vertex AI. Together AI Qwen2-7B-Instruct is available on Together AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 3n over Together AI Qwen2-7B-Instruct?
Gemma 3n is safer overall; choose Together AI Qwen2-7B-Instruct when long-context analysis matters. If your workload also depends on provider fit, start with Gemma 3n; if it depends on long-context analysis, run the same evaluation with Together AI Qwen2-7B-Instruct.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.