Llama 3.2 1B Instruct vs Llama 3.1 8B Instruct
Llama 3.2 1B Instruct (2024) and Llama 3.1 8B Instruct (2024) are compact production models from AI at Meta. Llama 3.2 1B Instruct ships a 128K-token context window, while Llama 3.1 8B Instruct ships a 128K-token context window. On MMLU PRO, Llama 3.1 8B Instruct leads by 24.3 pts. On pricing, Llama 3.1 8B Instruct costs $0.02/1M input tokens versus $0.03/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B Instruct is safer overall; choose Llama 3.1 8B Instruct when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.2 1B Instruct | Llama 3.1 8B Instruct |
|---|---|---|
| Decision fit | Coding, RAG, and Long context | RAG, Long context, and Classification |
| Context window | 128K | 128K |
| Cheapest output | $0.2/1M tokens | $0.05/1M tokens |
| Provider routes | 5 tracked | 12 tracked |
| Shared benchmarks | 2 rows | MMLU PRO leader |
Decision tradeoffs
- Local decision data tags Llama 3.2 1B Instruct for Coding, RAG, and Long context.
- Llama 3.1 8B Instruct leads the largest shared benchmark signal on MMLU PRO by 24.3 points.
- Llama 3.1 8B Instruct has the lower cheapest tracked output price at $0.05/1M tokens.
- Llama 3.1 8B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3.1 8B Instruct for RAG, Long context, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.2 1B Instruct
$71.60
Cheapest tracked route: OpenRouter
Llama 3.1 8B Instruct
$28.50
Cheapest tracked route: OpenRouter
Estimated monthly gap: $43.10. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI, NVIDIA NIM, and OpenRouter; start route-level A/B tests there.
- Llama 3.1 8B Instruct is $0.15/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Provider overlap exists on OpenRouter, Fireworks AI, and NVIDIA NIM; start route-level A/B tests there.
- Llama 3.2 1B Instruct is $0.15/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-09-25 | 2024-07-23 |
| Context window | 128K | 128K |
| Parameters | 1.23B | 8B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Llama 3.2 1B Instruct | Llama 3.1 8B Instruct |
|---|---|---|
| Input price | $0.03/1M tokens | $0.02/1M tokens |
| Output price | $0.2/1M tokens | $0.05/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.2 1B Instruct | Llama 3.1 8B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Llama 3.2 1B Instruct | Llama 3.1 8B Instruct |
|---|---|---|
| MMLU PRO | 20.0 | 44.3 |
| BFCL | 10.8 | 25.8 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 3.2 1B Instruct at 20 and Llama 3.1 8B Instruct at 44.3, with Llama 3.1 8B Instruct ahead by 24.3 points; BFCL has Llama 3.2 1B Instruct at 10.8 and Llama 3.1 8B Instruct at 25.8, with Llama 3.1 8B Instruct ahead by 15.0 points. The largest visible gap is 24.3 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 is close: both models cover structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Llama 3.2 1B Instruct lists $0.03/1M input and $0.2/1M output tokens, while Llama 3.1 8B Instruct lists $0.02/1M input and $0.05/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 8B Instruct lower by about $0.05 per million blended tokens. Availability is 5 providers versus 12, so concentration risk also matters.
Choose Llama 3.2 1B Instruct when provider fit are central to the workload. Choose Llama 3.1 8B 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, Llama 3.2 1B Instruct or Llama 3.1 8B Instruct?
Llama 3.2 1B Instruct supports 128K tokens, while Llama 3.1 8B 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 Instruct or Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct is cheaper on tracked token pricing. Llama 3.2 1B Instruct costs $0.03/1M input and $0.2/1M output tokens. Llama 3.1 8B Instruct costs $0.02/1M input and $0.05/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.2 1B Instruct or Llama 3.1 8B Instruct open source?
Llama 3.2 1B Instruct is listed under Open Source. Llama 3.1 8B Instruct 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, Llama 3.2 1B Instruct or Llama 3.1 8B Instruct?
Both Llama 3.2 1B Instruct and Llama 3.1 8B Instruct expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Llama 3.2 1B Instruct and Llama 3.1 8B Instruct?
Llama 3.2 1B Instruct is available on OpenRouter, Fireworks AI, NVIDIA NIM, Bitdeer AI, and AWS Bedrock. Llama 3.1 8B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, NVIDIA NIM, and GroqCloud. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.2 1B Instruct over Llama 3.1 8B Instruct?
Llama 3.2 1B Instruct is safer overall; choose Llama 3.1 8B Instruct when provider fit matters. If your workload also depends on provider fit, start with Llama 3.2 1B Instruct; if it depends on provider fit, run the same evaluation with Llama 3.1 8B Instruct.
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Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.