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Gemma 2 9B vs Phi 3.5 Mini Instruct

Gemma 2 9B (2024) and Phi 3.5 Mini 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 Mini Instruct ships a 128K-token context window. On pricing, Gemma 2 9B costs $0.06/1M input tokens versus $0.9/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Gemma 2 9B is ~1400% cheaper at $0.06/1M; pay for Phi 3.5 Mini Instruct only for long-context analysis.

Decision scorecard

Local evidence first
SignalGemma 2 9BPhi 3.5 Mini Instruct
Decision fitCoding, Classification, and JSON / Tool useLong context
Context window8K128K
Cheapest output$0.18/1M tokens$0.9/1M tokens
Provider routes3 tracked2 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Gemma 2 9B when...
  • 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.
Choose Phi 3.5 Mini Instruct when...
  • Phi 3.5 Mini Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Local decision data tags Phi 3.5 Mini Instruct for Long context.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output prices on this page.

Lower estimate Gemma 2 9B

Gemma 2 9B

$93.00

Cheapest tracked route: GCP Vertex AI

Phi 3.5 Mini Instruct

$945

Cheapest tracked route: Fireworks AI

Estimated monthly gap: $852. Batch, cache, and negotiated pricing are excluded from this local estimate.

Switch friction

Gemma 2 9B -> Phi 3.5 Mini Instruct
  • Provider overlap exists on Fireworks AI; start route-level A/B tests there.
  • Phi 3.5 Mini Instruct is $0.72/1M 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.
Phi 3.5 Mini Instruct -> Gemma 2 9B
  • Provider overlap exists on Fireworks AI; start route-level A/B tests there.
  • Gemma 2 9B is $0.72/1M 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
Released2024-06-272024-08-20
Context window8K128K
Parameters9B3.8B
Architecturedecoder onlydecoder only
LicenseOpen SourceMIT
Knowledge cutoff--

Pricing and availability

Pricing attributeGemma 2 9BPhi 3.5 Mini Instruct
Input price$0.06/1M tokens$0.9/1M tokens
Output price$0.18/1M tokens$0.9/1M tokens
Providers

Capabilities

CapabilityGemma 2 9BPhi 3.5 Mini Instruct
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsYesNo
Code executionNoNo

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 Mini Instruct lists $0.9/1M input and $0.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Gemma 2 9B lower by about $0.8 per million blended tokens. Availability is 3 providers versus 2, 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 Mini 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 Mini Instruct?

Phi 3.5 Mini 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 Mini 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 Mini Instruct costs $0.9/1M input and $0.9/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Gemma 2 9B or Phi 3.5 Mini Instruct open source?

Gemma 2 9B is listed under Open Source. Phi 3.5 Mini 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 Mini 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 Mini Instruct?

Gemma 2 9B is available on GCP Vertex AI, Fireworks AI, and Bitdeer AI. Phi 3.5 Mini Instruct is available on Fireworks AI and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Gemma 2 9B over Phi 3.5 Mini Instruct?

Gemma 2 9B is ~1400% cheaper at $0.06/1M; pay for Phi 3.5 Mini 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 Mini Instruct.

Continue comparing

Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.