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Gemma 3 12B Instruct vs Llama 3.1 70B Instruct

Gemma 3 12B Instruct (2025) and Llama 3.1 70B Instruct (2024) are compact production models from Google DeepMind and AI at Meta. Gemma 3 12B Instruct ships a 128K-token context window, while Llama 3.1 70B Instruct ships a 128K-token context window. On pricing, Gemma 3 12B Instruct costs $0.2/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Gemma 3 12B Instruct is ~100% cheaper at $0.2/1M; pay for Llama 3.1 70B Instruct only for provider fit.

Decision scorecard

Local evidence first
SignalGemma 3 12B InstructLlama 3.1 70B Instruct
Decision fitLong contextCoding, RAG, and Long context
Context window128K128K
Cheapest output$0.2/1M tokens$0.4/1M tokens
Provider routes1 tracked11 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Gemma 3 12B Instruct when...
  • Gemma 3 12B Instruct has the lower cheapest tracked output price at $0.2/1M tokens.
  • Local decision data tags Gemma 3 12B Instruct for Long context.
Choose Llama 3.1 70B Instruct when...
  • Llama 3.1 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
  • Llama 3.1 70B Instruct uniquely exposes Structured outputs in local model data.
  • Local decision data tags Llama 3.1 70B Instruct for Coding, RAG, and Long context.

Monthly cost at traffic

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

Lower estimate Gemma 3 12B Instruct

Gemma 3 12B Instruct

$210

Cheapest tracked route: Fireworks AI

Llama 3.1 70B Instruct

$420

Cheapest tracked route: Hyperbolic AI Inference

Estimated monthly gap: $210. Batch, cache, and negotiated pricing are excluded from this local estimate.

Switch friction

Gemma 3 12B Instruct -> Llama 3.1 70B Instruct
  • Provider overlap exists on Fireworks AI; start route-level A/B tests there.
  • Llama 3.1 70B Instruct is $0.2/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
  • Llama 3.1 70B Instruct adds Structured outputs in local capability data.
Llama 3.1 70B Instruct -> Gemma 3 12B Instruct
  • Provider overlap exists on Fireworks AI; start route-level A/B tests there.
  • Gemma 3 12B Instruct is $0.2/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
  • Check replacement coverage for Structured outputs before moving production traffic.

Specs

Specification
Released2025-01-012024-07-23
Context window128K128K
Parameters12B70B
Architecturedecoder onlydecoder only
LicenseOpen SourceOpen Source
Knowledge cutoff--

Pricing and availability

Pricing attributeGemma 3 12B InstructLlama 3.1 70B Instruct
Input price$0.2/1M tokens$0.4/1M tokens
Output price$0.2/1M tokens$0.4/1M tokens
Providers

Capabilities

CapabilityGemma 3 12B InstructLlama 3.1 70B Instruct
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsNoYes
Code executionNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on structured outputs: Llama 3.1 70B Instruct. 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 3 12B Instruct lists $0.2/1M input and $0.2/1M output tokens, while Llama 3.1 70B Instruct lists $0.4/1M input and $0.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Gemma 3 12B Instruct lower by about $0.2 per million blended tokens. Availability is 1 providers versus 11, so concentration risk also matters.

Choose Gemma 3 12B Instruct when provider fit and lower input-token cost are central to the workload. Choose Llama 3.1 70B Instruct 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.

FAQ

Which has a larger context window, Gemma 3 12B Instruct or Llama 3.1 70B Instruct?

Gemma 3 12B Instruct supports 128K tokens, while Llama 3.1 70B Instruct supports 128K 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 3 12B Instruct or Llama 3.1 70B Instruct?

Gemma 3 12B Instruct is cheaper on tracked token pricing. Gemma 3 12B Instruct costs $0.2/1M input and $0.2/1M output tokens. Llama 3.1 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Gemma 3 12B Instruct or Llama 3.1 70B Instruct open source?

Gemma 3 12B Instruct is listed under Open Source. Llama 3.1 70B 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 3 12B Instruct or Llama 3.1 70B Instruct?

Llama 3.1 70B Instruct 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 3 12B Instruct and Llama 3.1 70B Instruct?

Gemma 3 12B Instruct is available on Fireworks AI. Llama 3.1 70B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Gemma 3 12B Instruct over Llama 3.1 70B Instruct?

Gemma 3 12B Instruct is ~100% cheaper at $0.2/1M; pay for Llama 3.1 70B Instruct only for provider fit. If your workload also depends on provider fit, start with Gemma 3 12B Instruct; if it depends on provider fit, run the same evaluation with Llama 3.1 70B Instruct.

Continue comparing

Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.