Gemini 3.1 Pro Preview vs Llama 3 8B Instruct
Gemini 3.1 Pro Preview (2026) and Llama 3 8B 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 8B Instruct ships a 8k-token context window. On MMLU PRO, Gemini 3.1 Pro Preview leads by 50.5 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Gemini 3.1 Pro Preview fits 125x more tokens; pick it for long-context work and Llama 3 8B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemini 3.1 Pro Preview | Llama 3 8B Instruct |
|---|---|---|
| Best for | multimodal apps, tool-calling agents, and long-context analysis | provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, Classification, and JSON / Tool use |
| Context window | 1m | 8k |
| Cheapest output | $12/1M tokens | $0.05/1M tokens |
| Provider routes | 5 tracked | 17 tracked |
| Shared benchmarks | MMLU PRO leader | 4 shared |
Decision tradeoffs
- Gemini 3.1 Pro Preview holds a shared-benchmark lead on MMLU PRO, ahead by 50.5 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 8B Instruct has the lower cheapest tracked output price at $0.05/1M tokens.
- Llama 3 8B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3 8B Instruct for Coding, Classification, and JSON / Tool use.
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 8B Instruct
$28.50
Cheapest tracked route/tier: DeepInfra
Estimated monthly gap: $4,572. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on GCP Vertex AI, OpenRouter, and Replicate API; start route-level A/B tests there.
- Llama 3 8B Instruct is $11.95/1M 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, OpenRouter, and Replicate API; start route-level A/B tests there.
- Gemini 3.1 Pro Preview is $11.95/1M 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 | 2024-04-18 |
| Context window | 1m | 8k |
| Parameters | — | 8B |
| 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 | 2023-03 |
Pricing and availability
| Pricing attribute | Gemini 3.1 Pro Preview | Llama 3 8B Instruct |
|---|---|---|
| Input price |
| $0.02/1M tokens |
| Output price |
| $0.05/1M tokens |
| Providers |
Capabilities
| Capability | Gemini 3.1 Pro Preview | Llama 3 8B 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 8B Instruct |
|---|---|---|
| MMLU PRO | 91.0 | 40.5 |
| Google-Proof Q&A | 94.3 | 44.8 |
| HumanEval | 94.0 | 68.2 |
| Massive Multitask Language Understanding | 98.0 | 76.9 |
Deep dive
On shared benchmark coverage, MMLU PRO has Gemini 3.1 Pro Preview at 91.0 and Llama 3 8B Instruct at 40.5, with Gemini 3.1 Pro Preview ahead by 50.5 points; Google-Proof Q&A has Gemini 3.1 Pro Preview at 94.3 and Llama 3 8B Instruct at 44.8, with Gemini 3.1 Pro Preview ahead by 49.5 points; HumanEval has Gemini 3.1 Pro Preview at 94 and Llama 3 8B Instruct at 68.2, with Gemini 3.1 Pro Preview ahead by 25.8 points. The largest visible gap is 50.5 points on MMLU PRO, 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 8B Instruct lists $0.02/1M input and $0.05/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 8B Instruct lower by about $4.97 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 17, so concentration risk also matters.
Choose Gemini 3.1 Pro Preview when coding workflow support and larger context windows are central to the workload. Choose Llama 3 8B Instruct when provider fit, lower input-token cost, 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 8B Instruct?
Gemini 3.1 Pro Preview supports 1m tokens, while Llama 3 8B 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, Gemini 3.1 Pro Preview or Llama 3 8B 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 8B Instruct lists $0.02/1M input and $0.05/1M 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 8B Instruct open source?
Gemini 3.1 Pro Preview is listed under Proprietary. Llama 3 8B 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 8B 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 8B 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 8B Instruct?
Gemini 3.1 Pro Preview is available on Google AI Studio, GCP Vertex AI, OpenRouter, Replicate API, and Vercel AI Gateway. Llama 3 8B Instruct is available on AWS Bedrock, DeepInfra, OctoAI API (Deprecated), Fireworks AI, and Alibaba Cloud PAI-EAS. 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.