Firefunction V2 vs Gemma 3n
Firefunction V2 (2024) and Gemma 3n (2025) are compact production models from Fireworks AI and Google DeepMind. Firefunction V2 ships a 32k-token context window, while Gemma 3n ships a 32k-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.
Gemma 3n is safer overall; choose Firefunction V2 when provider fit matters.
Decision scorecard
Local evidence first| Signal | Firefunction V2 | Gemma 3n |
|---|---|---|
| Best for | general production evaluation | provider-routed production |
| Decision fit | General | Classification and JSON / Tool use |
| Context window | 32k | 32k |
| Cheapest output | $0.90/1M tokens | - |
| Provider routes | 1 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use Firefunction V2 when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- 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.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Firefunction V2
$945
Cheapest tracked route/tier: Fireworks AI
Gemma 3n
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 Firefunction V2 and Gemma 3n; plan for SDK, billing, or endpoint changes.
- Gemma 3n adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Gemma 3n and Firefunction V2; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-01-29 | 2025-03-12 |
| Context window | 32k | 32k |
| Parameters | 70B | — |
| Architecture | decoder only | decoder only |
| License | Unknown | Open Source |
| Knowledge cutoff | - | 2024-06 |
Pricing and availability
| Pricing attribute | Firefunction V2 | Gemma 3n |
|---|---|---|
| Input price | $0.90/1M tokens | - |
| Output price | $0.90/1M tokens | - |
| Providers |
Capabilities
| Capability | Firefunction V2 | Gemma 3n |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | 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 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: Firefunction V2 has $0.90/1M input tokens and Gemma 3n has no token price sourced yet. Provider availability is 1 tracked routes versus 2. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Firefunction V2 when provider fit are central to the workload. Choose Gemma 3n when provider fit 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, Firefunction V2 or Gemma 3n?
Firefunction V2 supports 32k 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 Firefunction V2 or Gemma 3n open source?
Firefunction V2 is listed under Unknown. Gemma 3n 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, Firefunction V2 or Gemma 3n?
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 Firefunction V2 and Gemma 3n?
Firefunction V2 is available on Fireworks AI. Gemma 3n is available on Google AI Studio and GCP Vertex AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Firefunction V2 over Gemma 3n?
Gemma 3n is safer overall; choose Firefunction V2 when provider fit matters. If your workload also depends on provider fit, start with Firefunction V2; if it depends on provider fit, run the same evaluation with Gemma 3n.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.