Gemma 7B Instruct vs Llama 3.2 90B Instruct
Gemma 7B Instruct (2024) and Llama 3.2 90B Instruct (2025) are compact production models from Google DeepMind and AI at Meta. Gemma 7B Instruct ships a 8k-token context window, while Llama 3.2 90B Instruct ships a 128k-token context window. On pricing, Gemma 7B Instruct costs $0.05/1M input tokens versus $1.35/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Gemma 7B Instruct is ~2600% cheaper at $0.05/1M; pay for Llama 3.2 90B Instruct only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Gemma 7B Instruct | Llama 3.2 90B Instruct |
|---|---|---|
| Best for | provider-routed production | multimodal apps |
| Decision fit | Coding, Classification, and JSON / Tool use | RAG, Long context, and Vision |
| Context window | 8k | 128k |
| Cheapest output | $0.25/1M tokens | $1.80/1M tokens |
| Provider routes | 8 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 7B Instruct has the lower cheapest tracked output price at $0.25/1M tokens.
- Gemma 7B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Gemma 7B Instruct for Coding, Classification, and JSON / Tool use.
- Llama 3.2 90B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.2 90B Instruct uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Llama 3.2 90B Instruct for RAG, Long context, and Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Gemma 7B Instruct
$103
Cheapest tracked route/tier: Replicate API
Llama 3.2 90B Instruct
$1,530
Cheapest tracked route/tier: AWS Bedrock
Estimated monthly gap: $1,428. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- No overlapping tracked provider route is sourced for Gemma 7B Instruct and Llama 3.2 90B Instruct; plan for SDK, billing, or endpoint changes.
- Llama 3.2 90B Instruct is $1.55/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Llama 3.2 90B Instruct adds Vision and Multimodal in local capability data.
- No overlapping tracked provider route is sourced for Llama 3.2 90B Instruct and Gemma 7B Instruct; plan for SDK, billing, or endpoint changes.
- Gemma 7B Instruct is $1.55/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-21 | 2025-09-01 |
| Context window | 8k | 128k |
| Parameters | 7B | 90B |
| Architecture | decoder only | - |
| License | Gemma | Llama 3 Community |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | 2023-04 | 2023-12 |
Pricing and availability
| Pricing attribute | Gemma 7B Instruct | Llama 3.2 90B Instruct |
|---|---|---|
| Input price | $0.05/1M tokens | $1.35/1M tokens |
| Output price | $0.25/1M tokens | $1.80/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 7B Instruct | Llama 3.2 90B Instruct |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | 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 vision: Llama 3.2 90B Instruct and multimodal input: Llama 3.2 90B Instruct. Both models share structured outputs, 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 7B Instruct lists $0.05/1M input and $0.25/1M output tokens on the cheapest tracked provider, while Llama 3.2 90B Instruct lists $1.35/1M input and $1.80/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Gemma 7B Instruct lower by about $1.38 per million blended tokens. Availability is 8 providers versus 1, so concentration risk also matters.
Choose Gemma 7B Instruct when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Llama 3.2 90B 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.
FAQ
Which has a larger context window, Gemma 7B Instruct or Llama 3.2 90B Instruct?
Llama 3.2 90B Instruct supports 128k tokens, while Gemma 7B Instruct 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 7B Instruct or Llama 3.2 90B Instruct?
Gemma 7B Instruct is cheaper on tracked token pricing. Gemma 7B Instruct costs $0.05/1M input and $0.25/1M output tokens. Llama 3.2 90B Instruct costs $1.35/1M input and $1.80/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Gemma 7B Instruct or Llama 3.2 90B Instruct open source?
Gemma 7B Instruct is listed under Gemma. Llama 3.2 90B Instruct is listed under Llama 3 Community. 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 7B Instruct or Llama 3.2 90B Instruct?
Llama 3.2 90B Instruct 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.
Which is better for multimodal input, Gemma 7B Instruct or Llama 3.2 90B Instruct?
Llama 3.2 90B Instruct has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Gemma 7B Instruct and Llama 3.2 90B Instruct?
Gemma 7B Instruct is available on NVIDIA NIM, Fireworks AI, Together AI, GCP Vertex AI, and Cloudflare Workers AI. Llama 3.2 90B Instruct is available on AWS Bedrock. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.