Gemini 2.5 Pro vs Llama 3 70B Instruct
Gemini 2.5 Pro (2025) and Llama 3 70B Instruct (2024) are frontier reasoning models from Google DeepMind and AI at Meta. Gemini 2.5 Pro ships a 1m-token context window, while Llama 3 70B Instruct ships a 8k-token context window. On MMLU PRO, Gemini 2.5 Pro leads by 28.8 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Gemini 2.5 Pro fits 125x more tokens; pick it for long-context work and Llama 3 70B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemini 2.5 Pro | Llama 3 70B Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, Classification, and JSON / Tool use |
| Context window | 1m | 8k |
| Cheapest output | $10/1M tokens | $0.40/1M tokens |
| Provider routes | 4 tracked | 18 tracked |
| Shared benchmarks | MMLU PRO leader | 2 rows |
Decision tradeoffs
- Gemini 2.5 Pro holds a shared-benchmark lead on MMLU PRO, ahead by 28.8 points.
- Gemini 2.5 Pro has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Gemini 2.5 Pro uniquely exposes Vision, Multimodal, and Reasoning in local model data.
- Local decision data tags Gemini 2.5 Pro for Coding, RAG, and Agents.
- Llama 3 70B Instruct has the lower cheapest tracked output price at $0.40/1M tokens.
- Llama 3 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3 70B 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 2.5 Pro
$3,500
Cheapest tracked route/tier: Google AI Studio <=200K tokens
Llama 3 70B Instruct
$420
Cheapest tracked route/tier: Hyperbolic AI Inference
Estimated monthly gap: $3,080. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on GCP Vertex AI and OpenRouter; start route-level A/B tests there.
- Llama 3 70B 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 Reasoning before moving production traffic.
- Provider overlap exists on GCP Vertex AI and OpenRouter; start route-level A/B tests there.
- Gemini 2.5 Pro is $9.60/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Gemini 2.5 Pro adds Vision, Multimodal, and Reasoning in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-06-17 | 2024-04-18 |
| Context window | 1m | 8k |
| Parameters | — | 70B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Llama 3 Community |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | 2025-01 | 2023-12 |
Pricing and availability
| Pricing attribute | Gemini 2.5 Pro | Llama 3 70B Instruct |
|---|---|---|
| Input price |
| $0.40/1M tokens |
| Output price |
| $0.40/1M tokens |
| Providers |
Capabilities
| Capability | Gemini 2.5 Pro | Llama 3 70B Instruct |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | Yes | 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 2.5 Pro | Llama 3 70B Instruct |
|---|---|---|
| MMLU PRO | 86.2 | 57.4 |
| HumanEval | 93.1 | 72.6 |
Deep dive
On shared benchmark coverage, MMLU PRO has Gemini 2.5 Pro at 86.2 and Llama 3 70B Instruct at 57.4, with Gemini 2.5 Pro ahead by 28.8 points; HumanEval has Gemini 2.5 Pro at 93.1 and Llama 3 70B Instruct at 72.6, with Gemini 2.5 Pro ahead by 20.5 points. The largest visible gap is 28.8 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 2.5 Pro, multimodal input: Gemini 2.5 Pro, reasoning mode: Gemini 2.5 Pro, function calling: Gemini 2.5 Pro, tool use: Gemini 2.5 Pro, and code execution: Gemini 2.5 Pro. 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 2.5 Pro lists tiered pricing: <=200K tokens is $1.25/1M input and $10/1M output; >200K tokens is $2.50/1M input and $15/1M output, while Llama 3 70B Instruct lists $0.40/1M input and $0.40/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 70B Instruct lower by about $3.48 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 4 providers versus 18, so concentration risk also matters.
Choose Gemini 2.5 Pro when coding workflow support and larger context windows are central to the workload. Choose Llama 3 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.
FAQ
Which has a larger context window, Gemini 2.5 Pro or Llama 3 70B Instruct?
Gemini 2.5 Pro supports 1m tokens, while Llama 3 70B 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 2.5 Pro or Llama 3 70B Instruct?
Gemini 2.5 Pro lists tiered pricing: <=200K tokens is $1.25/1M input and $10/1M output; >200K tokens is $2.50/1M input and $15/1M output. Llama 3 70B Instruct lists $0.40/1M input and $0.40/1M 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 2.5 Pro or Llama 3 70B Instruct open source?
Gemini 2.5 Pro is listed under Proprietary. Llama 3 70B 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 2.5 Pro or Llama 3 70B Instruct?
Gemini 2.5 Pro 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 2.5 Pro or Llama 3 70B Instruct?
Gemini 2.5 Pro 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 2.5 Pro and Llama 3 70B Instruct?
Gemini 2.5 Pro is available on Google AI Studio, GCP Vertex AI, OpenRouter, and Vercel AI Gateway. Llama 3 70B Instruct is available on GCP Vertex AI, AWS Bedrock, Microsoft Foundry, NVIDIA NIM, and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-05. Data sourced from public model cards and provider documentation.