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Gemini 2.5 Pro Preview 06-05 vs Llama 3.2 1B

Gemini 2.5 Pro Preview 06-05 (2025) and Llama 3.2 1B (2024) are compact production models from Google DeepMind and AI at Meta. Gemini 2.5 Pro Preview 06-05 ships a 1M-token context window, while Llama 3.2 1B ships a 128K-token context window. On pricing, Llama 3.2 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 3.2 1B is ~1150% cheaper at $0.1/1M; pay for Gemini 2.5 Pro Preview 06-05 only for coding workflow support.

Specs

Released2025-03-252024-09-25
Context window1M128K
Parameters1.23B
Architecturedecoder onlydecoder only
LicenseUnknownOpen Source
Knowledge cutoff-2023-12

Pricing and availability

Gemini 2.5 Pro Preview 06-05Llama 3.2 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 Preview 06-05Llama 3.2 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 structured outputs: Gemini 2.5 Pro Preview 06-05 and code execution: Gemini 2.5 Pro Preview 06-05. 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 Preview 06-05 lists $1.25/1M input and $10/1M output tokens, while Llama 3.2 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 3.2 1B lower by about $3.77 per million blended tokens. Availability is 1 providers versus 1, so concentration risk also matters.

Choose Gemini 2.5 Pro Preview 06-05 when coding workflow support and larger context windows are central to the workload. Choose Llama 3.2 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions.

FAQ

Which has a larger context window, Gemini 2.5 Pro Preview 06-05 or Llama 3.2 1B?

Gemini 2.5 Pro Preview 06-05 supports 1M tokens, while Llama 3.2 1B 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 2.5 Pro Preview 06-05 or Llama 3.2 1B?

Llama 3.2 1B is cheaper on tracked token pricing. Gemini 2.5 Pro Preview 06-05 costs $1.25/1M input and $10/1M output tokens. Llama 3.2 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 Preview 06-05 or Llama 3.2 1B open source?

Gemini 2.5 Pro Preview 06-05 is listed under Unknown. Llama 3.2 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 structured outputs, Gemini 2.5 Pro Preview 06-05 or Llama 3.2 1B?

Gemini 2.5 Pro Preview 06-05 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.

Which is better for code execution, Gemini 2.5 Pro Preview 06-05 or Llama 3.2 1B?

Gemini 2.5 Pro Preview 06-05 has the clearer documented code execution signal in this comparison. If code execution 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 Preview 06-05 and Llama 3.2 1B?

Gemini 2.5 Pro Preview 06-05 is available on OpenRouter. Llama 3.2 1B is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Continue comparing

Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.