Llama 3.2 1B Instruct vs Llama 3 70B Instruct
Llama 3.2 1B Instruct (2024) and Llama 3 70B Instruct (2024) are compact production models from AI at Meta. Llama 3.2 1B Instruct ships a 128k-token context window, while Llama 3 70B Instruct ships a 8k-token context window. On MMLU PRO, Llama 3 70B Instruct leads by 37.4 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 Instruct is ~1381% cheaper at $0.03/1M; pay for Llama 3 70B Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3.2 1B Instruct | Llama 3 70B Instruct |
|---|---|---|
| Best for | provider-routed production | provider-routed production |
| Decision fit | Coding, RAG, and Long context | Coding, Classification, and JSON / Tool use |
| Context window | 128k | 8k |
| Cheapest output | $0.20/1M tokens | $0.40/1M tokens |
| Provider routes | 7 tracked | 18 tracked |
| Shared benchmarks | 3 rows | MMLU PRO leader |
Decision tradeoffs
- Llama 3.2 1B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.2 1B Instruct has the lower cheapest tracked output price at $0.20/1M tokens.
- Local decision data tags Llama 3.2 1B Instruct for Coding, RAG, and Long context.
- Llama 3 70B Instruct holds a shared-benchmark lead on MMLU PRO, ahead by 37.4 points.
- Llama 3 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3 70B Instruct for Coding, Classification, and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.2 1B Instruct
$71.85
Cheapest tracked route/tier: Cloudflare Workers AI
Llama 3 70B Instruct
$420
Cheapest tracked route/tier: Hyperbolic AI Inference
Estimated monthly gap: $348. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on AWS Bedrock, NVIDIA NIM, and Fireworks AI; start route-level A/B tests there.
- Llama 3 70B Instruct is $0.20/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on OpenRouter, Fireworks AI, and NVIDIA NIM; start route-level A/B tests there.
- Llama 3.2 1B Instruct is $0.20/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-09-25 | 2024-04-18 |
| Context window | 128k | 8k |
| Parameters | 1.23B | 70B |
| 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 Instruct | Llama 3 70B Instruct |
|---|---|---|
| Input price | $0.03/1M tokens | $0.40/1M tokens |
| Output price | $0.20/1M tokens | $0.40/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.2 1B Instruct | Llama 3 70B 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 |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Llama 3.2 1B Instruct | Llama 3 70B Instruct |
|---|---|---|
| MMLU PRO | 20.0 | 57.4 |
| HumanEval | 28.1 | 72.6 |
| Massive Multitask Language Understanding | 49.3 | 82.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 3.2 1B Instruct at 20 and Llama 3 70B Instruct at 57.4, with Llama 3 70B Instruct ahead by 37.4 points; HumanEval has Llama 3.2 1B Instruct at 28.1 and Llama 3 70B Instruct at 72.6, with Llama 3 70B Instruct ahead by 44.5 points; Massive Multitask Language Understanding has Llama 3.2 1B Instruct at 49.3 and Llama 3 70B Instruct at 82, with Llama 3 70B Instruct ahead by 32.7 points. The largest visible gap is 44.5 points on HumanEval, 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 $0.20/1M output tokens on the cheapest tracked provider, while Llama 3 70B Instruct lists $0.40/1M input and $0.40/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B Instruct lower by about $0.32 per million blended tokens. Availability is 7 providers versus 18, so concentration risk also matters.
Choose Llama 3.2 1B Instruct when long-context analysis, larger context windows, and lower input-token cost are central to the workload. Choose Llama 3 70B 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 Instruct or Llama 3 70B Instruct?
Llama 3.2 1B Instruct supports 128k tokens, while Llama 3 70B Instruct supports 8k 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 70B Instruct?
Llama 3.2 1B Instruct is cheaper on tracked token pricing. Llama 3.2 1B Instruct costs $0.03/1M input and $0.20/1M output tokens. Llama 3 70B Instruct costs $0.40/1M input and $0.40/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.2 1B Instruct or Llama 3 70B Instruct open source?
Llama 3.2 1B Instruct is listed under Llama 3 Community. Llama 3 70B 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 Instruct or Llama 3 70B Instruct?
Both Llama 3.2 1B Instruct and Llama 3 70B 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 70B Instruct?
Llama 3.2 1B Instruct is available on Cloudflare Workers AI, OpenRouter, Fireworks AI, NVIDIA NIM, and Bitdeer AI. Llama 3 70B Instruct is available on GCP Vertex AI, AWS Bedrock, Microsoft Foundry, NVIDIA NIM, and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.2 1B Instruct over Llama 3 70B Instruct?
Llama 3.2 1B Instruct is ~1381% cheaper at $0.03/1M; pay for Llama 3 70B Instruct only for provider fit. If your workload also depends on long-context analysis, start with Llama 3.2 1B Instruct; if it depends on provider fit, run the same evaluation with Llama 3 70B Instruct.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.