Gemma 2 9B vs Phi 3.5 MoE Instruct
Gemma 2 9B (2024) and Phi 3.5 MoE Instruct (2024) are compact production models from Google DeepMind and Microsoft Research. Gemma 2 9B ships a 8K-token context window, while Phi 3.5 MoE Instruct ships a 128K-token context window. On pricing, Gemma 2 9B costs $0.06/1M input tokens versus $0.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Gemma 2 9B is ~733% cheaper at $0.06/1M; pay for Phi 3.5 MoE Instruct only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Gemma 2 9B | Phi 3.5 MoE Instruct |
|---|---|---|
| Decision fit | Coding, Classification, and JSON / Tool use | Long context |
| Context window | 8K | 128K |
| Cheapest output | $0.18/1M tokens | $0.5/1M tokens |
| Provider routes | 3 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 2 9B has the lower cheapest tracked output price at $0.18/1M tokens.
- Gemma 2 9B has broader tracked provider coverage for fallback and procurement flexibility.
- Gemma 2 9B uniquely exposes Structured outputs in local model data.
- Local decision data tags Gemma 2 9B for Coding, Classification, and JSON / Tool use.
- Phi 3.5 MoE Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Phi 3.5 MoE Instruct for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Gemma 2 9B
$93.00
Cheapest tracked route: GCP Vertex AI
Phi 3.5 MoE Instruct
$525
Cheapest tracked route: Fireworks AI
Estimated monthly gap: $432. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Phi 3.5 MoE Instruct is $0.32/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Gemma 2 9B is $0.32/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Gemma 2 9B adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-06-27 | 2024-08-20 |
| Context window | 8K | 128K |
| Parameters | 9B | 16x3.8B (42B, 6.6B active) |
| Architecture | decoder only | decoder only |
| License | Open Source | MIT |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 2 9B | Phi 3.5 MoE Instruct |
|---|---|---|
| Input price | $0.06/1M tokens | $0.5/1M tokens |
| Output price | $0.18/1M tokens | $0.5/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 2 9B | Phi 3.5 MoE Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Gemma 2 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.
For cost, Gemma 2 9B lists $0.06/1M input and $0.18/1M output tokens, while Phi 3.5 MoE Instruct lists $0.5/1M input and $0.5/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Gemma 2 9B lower by about $0.4 per million blended tokens. Availability is 3 providers versus 1, so concentration risk also matters.
Choose Gemma 2 9B when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Phi 3.5 MoE 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.
FAQ
Which has a larger context window, Gemma 2 9B or Phi 3.5 MoE Instruct?
Phi 3.5 MoE Instruct supports 128K tokens, while Gemma 2 9B supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Gemma 2 9B or Phi 3.5 MoE Instruct?
Gemma 2 9B is cheaper on tracked token pricing. Gemma 2 9B costs $0.06/1M input and $0.18/1M output tokens. Phi 3.5 MoE Instruct costs $0.5/1M input and $0.5/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Gemma 2 9B or Phi 3.5 MoE Instruct open source?
Gemma 2 9B is listed under Open Source. Phi 3.5 MoE Instruct is listed under MIT. 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 2 9B or Phi 3.5 MoE Instruct?
Gemma 2 9B 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 Gemma 2 9B and Phi 3.5 MoE Instruct?
Gemma 2 9B is available on GCP Vertex AI, Fireworks AI, and Bitdeer AI. Phi 3.5 MoE Instruct is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 2 9B over Phi 3.5 MoE Instruct?
Gemma 2 9B is ~733% cheaper at $0.06/1M; pay for Phi 3.5 MoE Instruct only for long-context analysis. If your workload also depends on provider fit, start with Gemma 2 9B; if it depends on long-context analysis, run the same evaluation with Phi 3.5 MoE Instruct.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.