Gemini 1.5 Pro vs Llama 3.1 70B Instruct
Gemini 1.5 Pro (2024) and Llama 3.1 70B Instruct (2024) are compact production models from Google DeepMind and AI at Meta. Gemini 1.5 Pro ships a 2M-token context window, while Llama 3.1 70B Instruct ships a 128K-token context window. On pricing, Llama 3.1 70B Instruct costs $0.4/1M input tokens versus $1.25/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.1 70B Instruct is ~213% cheaper at $0.4/1M; pay for Gemini 1.5 Pro only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Gemini 1.5 Pro | Llama 3.1 70B Instruct |
|---|---|---|
| Decision fit | RAG, Long context, and Vision | Coding, RAG, and Long context |
| Context window | 2M | 128K |
| Cheapest output | $5/1M tokens | $0.4/1M tokens |
| Provider routes | 2 tracked | 11 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemini 1.5 Pro has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Gemini 1.5 Pro for RAG, Long context, and Vision.
- Llama 3.1 70B Instruct has the lower cheapest tracked output price at $0.4/1M tokens.
- 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.
Gemini 1.5 Pro
$2,250
Cheapest tracked route: GCP Vertex AI
Llama 3.1 70B Instruct
$420
Cheapest tracked route: Hyperbolic AI Inference
Estimated monthly gap: $1,830. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- No overlapping tracked provider route is sourced for Gemini 1.5 Pro and Llama 3.1 70B Instruct; plan for SDK, billing, or endpoint changes.
- Llama 3.1 70B Instruct is $4.6/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- No overlapping tracked provider route is sourced for Llama 3.1 70B Instruct and Gemini 1.5 Pro; plan for SDK, billing, or endpoint changes.
- Gemini 1.5 Pro is $4.6/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-15 | 2024-07-23 |
| Context window | 2M | 128K |
| Parameters | — | 70B |
| Architecture | decoder only | decoder only |
| License | Unknown | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemini 1.5 Pro | Llama 3.1 70B Instruct |
|---|---|---|
| Input price | $1.25/1M tokens | $0.4/1M tokens |
| Output price | $5/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Capability | Gemini 1.5 Pro | 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, Gemini 1.5 Pro lists $1.25/1M input and $5/1M 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 Llama 3.1 70B Instruct lower by about $1.98 per million blended tokens. Availability is 2 providers versus 11, so concentration risk also matters.
Choose Gemini 1.5 Pro when long-context analysis and larger context windows are central to the workload. Choose Llama 3.1 70B 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. 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.
FAQ
Which has a larger context window, Gemini 1.5 Pro or Llama 3.1 70B Instruct?
Gemini 1.5 Pro supports 2M tokens, while Llama 3.1 70B 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 1.5 Pro or Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct is cheaper on tracked token pricing. Gemini 1.5 Pro costs $1.25/1M input and $5/1M 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 Gemini 1.5 Pro or Llama 3.1 70B Instruct open source?
Gemini 1.5 Pro 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 or Llama 3.1 70B Instruct?
Both Gemini 1.5 Pro 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 Gemini 1.5 Pro and Llama 3.1 70B Instruct?
Gemini 1.5 Pro is available on GCP Vertex AI and Google AI Studio. 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 over Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct is ~213% cheaper at $0.4/1M; pay for Gemini 1.5 Pro only for long-context analysis. If your workload also depends on long-context analysis, start with Gemini 1.5 Pro; 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.