Gemma 3 vs Llama 3.1 70B Instruct
Gemma 3 (2025) and Llama 3.1 70B Instruct (2024) are compact production models from Google DeepMind and AI at Meta. Gemma 3 ships a not-yet-sourced context window, while Llama 3.1 70B Instruct ships a 128K-token context window. On pricing, Gemma 3 costs $0.04/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Gemma 3 is ~900% cheaper at $0.04/1M; pay for Llama 3.1 70B Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | Gemma 3 | Llama 3.1 70B Instruct |
|---|---|---|
| Decision fit | Classification and JSON / Tool use | Coding, RAG, and Long context |
| Context window | — | 128K |
| Cheapest output | $0.08/1M tokens | $0.4/1M tokens |
| Provider routes | 3 tracked | 11 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 3 has the lower cheapest tracked output price at $0.08/1M tokens.
- Local decision data tags Gemma 3 for Classification and JSON / Tool use.
- Llama 3.1 70B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.1 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- 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.
Gemma 3
$52.00
Cheapest tracked route: OpenRouter
Llama 3.1 70B Instruct
$420
Cheapest tracked route: Hyperbolic AI Inference
Estimated monthly gap: $368. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Llama 3.1 70B Instruct is $0.32/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Gemma 3 is $0.32/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-12 | 2024-07-23 |
| Context window | — | 128K |
| Parameters | — | 70B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 3 | Llama 3.1 70B Instruct |
|---|---|---|
| Input price | $0.04/1M tokens | $0.4/1M tokens |
| Output price | $0.08/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 3 | Llama 3.1 70B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint is close: both models cover structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Gemma 3 lists $0.04/1M input and $0.08/1M 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 lower by about $0.35 per million blended tokens. Availability is 3 providers versus 11, so concentration risk also matters.
Choose Gemma 3 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which is cheaper, Gemma 3 or Llama 3.1 70B Instruct?
Gemma 3 is cheaper on tracked token pricing. Gemma 3 costs $0.04/1M input and $0.08/1M 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 or Llama 3.1 70B Instruct open source?
Gemma 3 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 or Llama 3.1 70B Instruct?
Both Gemma 3 and Llama 3.1 70B Instruct expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Gemma 3 and Llama 3.1 70B Instruct?
Gemma 3 is available on OpenRouter, Google AI Studio, and GCP Vertex 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 over Llama 3.1 70B Instruct?
Gemma 3 is ~900% cheaper at $0.04/1M; pay for Llama 3.1 70B Instruct only for provider fit. If your workload also depends on provider fit, start with Gemma 3; 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.