Gemini 3.1 Pro Preview vs Llama 3.1 405B Instruct
Gemini 3.1 Pro Preview (2026) and Llama 3.1 405B Instruct (2024) are compact production models from Google DeepMind and AI at Meta. Gemini 3.1 Pro Preview ships a 1m-token context window, while Llama 3.1 405B Instruct ships a 128k-token context window. On Massive Multitask Language Understanding, Gemini 3.1 Pro Preview leads by 9.4 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Pick Gemini 3.1 Pro Preview for general evaluation; Llama 3.1 405B Instruct is better when provider fit matters more.
Decision scorecard
Local evidence first| Signal | Gemini 3.1 Pro Preview | Llama 3.1 405B Instruct |
|---|---|---|
| Best for | multimodal apps, tool-calling agents, and long-context analysis | provider-routed production |
| Decision fit | Coding, RAG, and Agents | RAG, Long context, and Classification |
| Context window | 1m | 128k |
| Cheapest output | $12/1M tokens | $2.40/1M tokens |
| Provider routes | 5 tracked | 11 tracked |
| Shared benchmarks | Massive Multitask Language Understanding leader | 1 shared |
Decision tradeoffs
- Gemini 3.1 Pro Preview holds a shared-benchmark lead on Massive Multitask Language Understanding, ahead by 9.4 points.
- Gemini 3.1 Pro Preview has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Gemini 3.1 Pro Preview uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Gemini 3.1 Pro Preview for Coding, RAG, and Agents.
- Llama 3.1 405B Instruct has the lower cheapest tracked output price at $2.40/1M tokens.
- 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 route or tier on this page.
Gemini 3.1 Pro Preview
$4,600
Cheapest tracked route/tier: Google AI Studio
Llama 3.1 405B Instruct
$2,520
Cheapest tracked route/tier: AWS Bedrock
Estimated monthly gap: $2,080. Batch, cache, alternate speed tiers, 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 $9.60/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
- Provider overlap exists on GCP Vertex AI; start route-level A/B tests there.
- Gemini 3.1 Pro Preview is $9.60/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Gemini 3.1 Pro Preview adds Vision, Multimodal, and Function calling in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-19 | 2024-07-23 |
| Context window | 1m | 128k |
| Parameters | — | 405B |
| Architecture | Decoder Only | Decoder Only |
| License | Proprietary | Llama 3 Community |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use: conditional | Commercial use: conditional |
| Knowledge cutoff | 2025-01 | 2023-12 |
Pricing and availability
| Pricing attribute | Gemini 3.1 Pro Preview | Llama 3.1 405B Instruct |
|---|---|---|
| Input price |
| $2.40/1M tokens |
| Output price |
| $2.40/1M tokens |
| Providers |
Capabilities
| Capability | Gemini 3.1 Pro Preview | Llama 3.1 405B Instruct |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Gemini 3.1 Pro Preview | Llama 3.1 405B Instruct |
|---|---|---|
| Massive Multitask Language Understanding | 98.0 | 88.6 |
Deep dive
On shared benchmark coverage, Massive Multitask Language Understanding has Gemini 3.1 Pro Preview at 98 and Llama 3.1 405B Instruct at 88.6, with Gemini 3.1 Pro Preview ahead by 9.4 points. The largest visible gap is 9.4 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 differs most on vision: Gemini 3.1 Pro Preview, multimodal input: Gemini 3.1 Pro Preview, function calling: Gemini 3.1 Pro Preview, tool use: Gemini 3.1 Pro Preview, and code execution: Gemini 3.1 Pro Preview. 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, Gemini 3.1 Pro Preview lists tiered pricing: 0-200,001t is $2/1M input and $12/1M output; 200,001t+ is $4/1M input and $18/1M output, while Llama 3.1 405B Instruct lists $2.40/1M input and $2.40/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 405B Instruct lower by about $2.60 per million blended tokens. For tiered rows, this cheapest-track view can understate interactive or fast-lane spend, so compare the tier you will actually use. Availability is 5 providers versus 11, so concentration risk also matters.
Choose Gemini 3.1 Pro Preview when coding workflow support, larger context windows, and lower cheapest-tier input-token cost are central to the workload. Choose Llama 3.1 405B 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.
FAQ
Which has a larger context window, Gemini 3.1 Pro Preview or Llama 3.1 405B Instruct?
Gemini 3.1 Pro Preview supports 1m tokens, while Llama 3.1 405B 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, Gemini 3.1 Pro Preview or Llama 3.1 405B Instruct?
Gemini 3.1 Pro Preview lists tiered pricing: 0-200,001t is $2/1M input and $12/1M output; 200,001t+ is $4/1M input and $18/1M output. Llama 3.1 405B Instruct lists $2.40/1M input and $2.40/1M output tokens on the cheapest tracked provider. Compare the tier you will actually use; cheap async pricing can overstate savings for interactive workflows. Provider discounts or batch pricing can still change the final bill.
Is Gemini 3.1 Pro Preview or Llama 3.1 405B Instruct open source?
Gemini 3.1 Pro Preview is listed under Proprietary. Llama 3.1 405B 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, Gemini 3.1 Pro Preview or Llama 3.1 405B Instruct?
Gemini 3.1 Pro Preview 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, Gemini 3.1 Pro Preview or Llama 3.1 405B Instruct?
Gemini 3.1 Pro Preview 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 Gemini 3.1 Pro Preview and Llama 3.1 405B Instruct?
Gemini 3.1 Pro Preview is available on Google AI Studio, GCP Vertex AI, OpenRouter, Replicate API, and Vercel AI Gateway. 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.
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Last reviewed: 2026-06-19. Data sourced from public model cards and provider documentation.