Llama 3.2 1B vs Llama 3.1 405B Instruct
Llama 3.2 1B (2024) and Llama 3.1 405B Instruct (2024) are compact production models from AI at Meta. Llama 3.2 1B ships a 128k-token context window, while Llama 3.1 405B Instruct ships a 128k-token context window. On Massive Multitask Language Understanding, Llama 3.1 405B Instruct leads by 34.4 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 3.2 1B is ~2300% cheaper at $0.10/1M; pay for Llama 3.1 405B Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3.2 1B | Llama 3.1 405B Instruct |
|---|---|---|
| Best for | general production evaluation | provider-routed production |
| Decision fit | Coding, Long context, and Classification | RAG, Long context, and Classification |
| Context window | 128k | 128k |
| Cheapest output | $0.10/1M tokens | $2.40/1M tokens |
| Provider routes | 1 tracked | 11 tracked |
| Shared benchmarks | 1 rows | Massive Multitask Language Understanding leader |
Decision tradeoffs
- Llama 3.2 1B has the lower cheapest tracked output price at $0.10/1M tokens.
- Local decision data tags Llama 3.2 1B for Coding, Long context, and Classification.
- Llama 3.1 405B Instruct holds a shared-benchmark lead on Massive Multitask Language Understanding, ahead by 34.4 points.
- Llama 3.1 405B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3.1 405B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3.1 405B Instruct for RAG, Long context, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.2 1B
$105
Cheapest tracked route/tier: Fireworks AI
Llama 3.1 405B Instruct
$2,520
Cheapest tracked route/tier: AWS Bedrock
Estimated monthly gap: $2,415. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Llama 3.1 405B Instruct is $2.30/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Llama 3.1 405B Instruct adds Structured outputs in local capability data.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Llama 3.2 1B is $2.30/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-09-25 | 2024-07-23 |
| Context window | 128k | 128k |
| Parameters | 1.23B | 405B |
| Architecture | decoder only | decoder only |
| License | Llama 3 Community | Llama 3 Community |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | 2023-12 | 2023-12 |
Pricing and availability
| Pricing attribute | Llama 3.2 1B | Llama 3.1 405B Instruct |
|---|---|---|
| Input price | $0.10/1M tokens | $2.40/1M tokens |
| Output price | $0.10/1M tokens | $2.40/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.2 1B | Llama 3.1 405B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Llama 3.2 1B | Llama 3.1 405B Instruct |
|---|---|---|
| Massive Multitask Language Understanding | 54.2 | 88.6 |
Deep dive
On shared benchmark coverage, Massive Multitask Language Understanding has Llama 3.2 1B at 54.2 and Llama 3.1 405B Instruct at 88.6, with Llama 3.1 405B Instruct ahead by 34.4 points. The largest visible gap is 34.4 points on Massive Multitask Language Understanding, 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 structured outputs: Llama 3.1 405B Instruct. 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, Llama 3.2 1B lists $0.10/1M input and $0.10/1M output tokens on the cheapest tracked provider, while Llama 3.1 405B Instruct lists $2.40/1M input and $2.40/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B lower by about $2.30 per million blended tokens. Availability is 1 providers versus 11, so concentration risk also matters.
Choose Llama 3.2 1B when provider fit and lower input-token cost are central to the workload. Choose Llama 3.1 405B Instruct when provider fit 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, Llama 3.2 1B or Llama 3.1 405B Instruct?
Llama 3.2 1B supports 128k tokens, while Llama 3.1 405B 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, Llama 3.2 1B or Llama 3.1 405B Instruct?
Llama 3.2 1B is cheaper on tracked token pricing. Llama 3.2 1B costs $0.10/1M input and $0.10/1M output tokens. Llama 3.1 405B Instruct costs $2.40/1M input and $2.40/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.2 1B or Llama 3.1 405B Instruct open source?
Llama 3.2 1B is listed under Llama 3 Community. Llama 3.1 405B 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 structured outputs, Llama 3.2 1B or Llama 3.1 405B Instruct?
Llama 3.1 405B Instruct 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.
Where can I run Llama 3.2 1B and Llama 3.1 405B Instruct?
Llama 3.2 1B is available on Fireworks AI. Llama 3.1 405B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, IBM watsonx, and Scale AI GenAI Platform. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.2 1B over Llama 3.1 405B Instruct?
Llama 3.2 1B is ~2300% cheaper at $0.10/1M; pay for Llama 3.1 405B Instruct only for provider fit. If your workload also depends on provider fit, start with Llama 3.2 1B; if it depends on provider fit, run the same evaluation with Llama 3.1 405B Instruct.
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Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.