Gemini 1.5 Pro Experimental 0801 vs Llama 3.1 70B Instruct
Gemini 1.5 Pro Experimental 0801 (2024) and Llama 3.1 70B Instruct (2024) are compact production models from Google DeepMind and AI at Meta. Gemini 1.5 Pro Experimental 0801 ships a not-yet-sourced context window, while Llama 3.1 70B Instruct ships a 128K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Gemini 1.5 Pro Experimental 0801 is safer overall; choose Llama 3.1 70B Instruct when provider fit matters.
Decision scorecard
Local evidence first| Signal | Gemini 1.5 Pro Experimental 0801 | Llama 3.1 70B Instruct |
|---|---|---|
| Decision fit | General | Coding, RAG, and Long context |
| Context window | — | 128K |
| Cheapest output | - | $0.4/1M tokens |
| Provider routes | 0 tracked | 11 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use Gemini 1.5 Pro Experimental 0801 when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- 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.
- 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.
Gemini 1.5 Pro Experimental 0801
Unavailable
No complete token price in local provider data
Llama 3.1 70B Instruct
$420
Cheapest tracked route: Hyperbolic AI Inference
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Gemini 1.5 Pro Experimental 0801 and Llama 3.1 70B Instruct; plan for SDK, billing, or endpoint changes.
- Llama 3.1 70B Instruct adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Llama 3.1 70B Instruct and Gemini 1.5 Pro Experimental 0801; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-08-01 | 2024-07-23 |
| Context window | — | 128K |
| Parameters | — | 70B |
| Architecture | decoder only | decoder only |
| License | Unknown | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemini 1.5 Pro Experimental 0801 | Llama 3.1 70B Instruct |
|---|---|---|
| Input price | - | $0.4/1M tokens |
| Output price | - | $0.4/1M tokens |
| Providers | - |
Capabilities
| Capability | Gemini 1.5 Pro Experimental 0801 | Llama 3.1 70B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
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.
Pricing coverage is uneven: Gemini 1.5 Pro Experimental 0801 has no token price sourced yet and Llama 3.1 70B Instruct has $0.4/1M input tokens. Provider availability is 0 tracked routes versus 11. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemini 1.5 Pro Experimental 0801 when provider fit 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
Is Gemini 1.5 Pro Experimental 0801 or Llama 3.1 70B Instruct open source?
Gemini 1.5 Pro Experimental 0801 is listed under Unknown. 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, Gemini 1.5 Pro Experimental 0801 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 Gemini 1.5 Pro Experimental 0801 and Llama 3.1 70B Instruct?
Gemini 1.5 Pro Experimental 0801 is available on the tracked providers still being sourced. 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 Gemini 1.5 Pro Experimental 0801 over Llama 3.1 70B Instruct?
Gemini 1.5 Pro Experimental 0801 is safer overall; choose Llama 3.1 70B Instruct when provider fit matters. If your workload also depends on provider fit, start with Gemini 1.5 Pro Experimental 0801; 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.