Gemma 2 27B vs Llama 3.1 405B Instruct
Gemma 2 27B (2024) and Llama 3.1 405B Instruct (2024) are compact production models from Google DeepMind and AI at Meta. Gemma 2 27B ships a 8K-token context window, while Llama 3.1 405B Instruct ships a 128K-token context window. On Massive Multitask Language Understanding, Llama 3.1 405B Instruct leads by 7 pts. On pricing, Gemma 2 27B costs $0.08/1M input tokens versus $2.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Gemma 2 27B is ~2900% cheaper at $0.08/1M; pay for Llama 3.1 405B Instruct only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Gemma 2 27B | Llama 3.1 405B Instruct |
|---|---|---|
| Decision fit | Coding, Classification, and JSON / Tool use | RAG, Long context, and Classification |
| Context window | 8K | 128K |
| Cheapest output | $0.24/1M tokens | $2.4/1M tokens |
| Provider routes | 2 tracked | 11 tracked |
| Shared benchmarks | 1 rows | Massive Multitask Language Understanding leader |
Decision tradeoffs
- Gemma 2 27B has the lower cheapest tracked output price at $0.24/1M tokens.
- Local decision data tags Gemma 2 27B for Coding, Classification, and JSON / Tool use.
- Llama 3.1 405B Instruct leads the largest shared benchmark signal on Massive Multitask Language Understanding by 7 points.
- Llama 3.1 405B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.1 405B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3.1 405B Instruct for RAG, Long context, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Gemma 2 27B
$124
Cheapest tracked route: Bitdeer AI
Llama 3.1 405B Instruct
$2,520
Cheapest tracked route: AWS Bedrock
Estimated monthly gap: $2,396. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on GCP Vertex AI; start route-level A/B tests there.
- Llama 3.1 405B Instruct is $2.16/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on GCP Vertex AI; start route-level A/B tests there.
- Gemma 2 27B is $2.16/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-06-27 | 2024-07-23 |
| Context window | 8K | 128K |
| Parameters | 27B | 405B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 2 27B | Llama 3.1 405B Instruct |
|---|---|---|
| Input price | $0.08/1M tokens | $2.4/1M tokens |
| Output price | $0.24/1M tokens | $2.4/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 2 27B | Llama 3.1 405B 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
| Benchmark | Gemma 2 27B | Llama 3.1 405B Instruct |
|---|---|---|
| Massive Multitask Language Understanding | 81.6 | 88.6 |
Deep dive
On shared benchmark coverage, Massive Multitask Language Understanding has Gemma 2 27B at 81.6 and Llama 3.1 405B Instruct at 88.6, with Llama 3.1 405B Instruct ahead by 7 points. The largest visible gap is 7 points on Massive Multitask Language Understanding, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
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 2 27B lists $0.08/1M input and $0.24/1M output tokens, while Llama 3.1 405B Instruct lists $2.4/1M input and $2.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Gemma 2 27B lower by about $2.27 per million blended tokens. Availability is 2 providers versus 11, so concentration risk also matters.
Choose Gemma 2 27B when provider fit and lower input-token cost are central to the workload. Choose Llama 3.1 405B Instruct when long-context analysis, larger context windows, 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.
FAQ
Which has a larger context window, Gemma 2 27B or Llama 3.1 405B Instruct?
Llama 3.1 405B Instruct supports 128K tokens, while Gemma 2 27B 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 27B or Llama 3.1 405B Instruct?
Gemma 2 27B is cheaper on tracked token pricing. Gemma 2 27B costs $0.08/1M input and $0.24/1M output tokens. Llama 3.1 405B Instruct costs $2.4/1M input and $2.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Gemma 2 27B or Llama 3.1 405B Instruct open source?
Gemma 2 27B is listed under Open Source. Llama 3.1 405B 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 2 27B or Llama 3.1 405B Instruct?
Both Gemma 2 27B and Llama 3.1 405B 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 2 27B and Llama 3.1 405B Instruct?
Gemma 2 27B is available on GCP Vertex AI and Bitdeer AI. Llama 3.1 405B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, IBM watsonx, and Scale AI GenAI Platform. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 2 27B over Llama 3.1 405B Instruct?
Gemma 2 27B is ~2900% cheaper at $0.08/1M; pay for Llama 3.1 405B Instruct only for long-context analysis. If your workload also depends on provider fit, start with Gemma 2 27B; if it depends on long-context analysis, run the same evaluation with Llama 3.1 405B Instruct.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.