Claude Opus 4.5 vs Llama 3.2 1B
Claude Opus 4.5 (2025) and Llama 3.2 1B (2024) are frontier reasoning models from Anthropic and AI at Meta. Claude Opus 4.5 ships a 200K-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 $5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B is ~4900% cheaper at $0.1/1M; pay for Claude Opus 4.5 only for coding workflow support.
Specs
| Released | 2025-11-01 | 2024-09-25 |
| Context window | 200K | 128K |
| Parameters | — | 1.23B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Open Source |
| Knowledge cutoff | 2025-12 | 2023-12 |
Pricing and availability
| Claude Opus 4.5 | Llama 3.2 1B | |
|---|---|---|
| Input price | $5/1M tokens | $0.1/1M tokens |
| Output price | $25/1M tokens | $0.1/1M tokens |
| Providers |
Capabilities
| Claude Opus 4.5 | Llama 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 vision: Claude Opus 4.5, multimodal input: Claude Opus 4.5, reasoning mode: Claude Opus 4.5, function calling: Claude Opus 4.5, tool use: Claude Opus 4.5, structured outputs: Claude Opus 4.5, and code execution: Claude Opus 4.5. 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, Claude Opus 4.5 lists $5/1M input and $25/1M 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 $10.90 per million blended tokens. Availability is 6 providers versus 1, so concentration risk also matters.
Choose Claude Opus 4.5 when coding workflow support, larger context windows, and broader provider choice 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.
FAQ
Which has a larger context window, Claude Opus 4.5 or Llama 3.2 1B?
Claude Opus 4.5 supports 200K 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, Claude Opus 4.5 or Llama 3.2 1B?
Llama 3.2 1B is cheaper on tracked token pricing. Claude Opus 4.5 costs $5/1M input and $25/1M 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 Claude Opus 4.5 or Llama 3.2 1B open source?
Claude Opus 4.5 is listed under Proprietary. 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 vision, Claude Opus 4.5 or Llama 3.2 1B?
Claude Opus 4.5 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, Claude Opus 4.5 or Llama 3.2 1B?
Claude Opus 4.5 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 Claude Opus 4.5 and Llama 3.2 1B?
Claude Opus 4.5 is available on Microsoft Foundry, Anthropic, GCP Vertex AI, AWS Bedrock, and OpenRouter. Llama 3.2 1B is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.