Gemini 2.5 Pro vs Llama Guard 3 1B
Gemini 2.5 Pro (2025) and Llama Guard 3 1B (2024) are general-purpose language models from Google DeepMind and AI at Meta. Gemini 2.5 Pro ships a 1M-token context window, while Llama Guard 3 1B ships a not-yet-sourced context window. On pricing, Llama Guard 3 1B costs $0.1/1M input tokens versus $1.25/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama Guard 3 1B is ~1150% cheaper at $0.1/1M; pay for Gemini 2.5 Pro only for coding workflow support.
Specs
| Released | 2025-06-17 | 2024-09-25 |
| Context window | 1M | — |
| Parameters | — | 1B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Open Source |
| Knowledge cutoff | 2025-01 | - |
Pricing and availability
| Gemini 2.5 Pro | Llama Guard 3 1B | |
|---|---|---|
| Input price | $1.25/1M tokens | $0.1/1M tokens |
| Output price | $10/1M tokens | $0.1/1M tokens |
| Providers |
Capabilities
| Gemini 2.5 Pro | Llama Guard 3 1B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Gemini 2.5 Pro, multimodal input: Gemini 2.5 Pro, function calling: Gemini 2.5 Pro, tool use: Gemini 2.5 Pro, structured outputs: Gemini 2.5 Pro, and code execution: Gemini 2.5 Pro. 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.
For cost, Gemini 2.5 Pro lists $1.25/1M input and $10/1M output tokens, while Llama Guard 3 1B lists $0.1/1M input and $0.1/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama Guard 3 1B lower by about $3.77 per million blended tokens. Availability is 3 providers versus 1, so concentration risk also matters.
Choose Gemini 2.5 Pro when coding workflow support and broader provider choice are central to the workload. Choose Llama Guard 3 1B when provider fit and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which is cheaper, Gemini 2.5 Pro or Llama Guard 3 1B?
Llama Guard 3 1B is cheaper on tracked token pricing. Gemini 2.5 Pro costs $1.25/1M input and $10/1M output tokens. Llama Guard 3 1B costs $0.1/1M input and $0.1/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Gemini 2.5 Pro or Llama Guard 3 1B open source?
Gemini 2.5 Pro is listed under Proprietary. Llama Guard 3 1B 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 vision, Gemini 2.5 Pro or Llama Guard 3 1B?
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 Guard 3 1B?
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.
Which is better for function calling, Gemini 2.5 Pro or Llama Guard 3 1B?
Gemini 2.5 Pro has the clearer documented function calling signal in this comparison. If function calling 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 Guard 3 1B?
Gemini 2.5 Pro is available on Google AI Studio, GCP Vertex AI, and OpenRouter. Llama Guard 3 1B is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.