Llama 2 13B Chat vs Llama 3 70B Instruct
Llama 2 13B Chat (2023) and Llama 3 70B Instruct (2024) are compact production models from AI at Meta. Llama 2 13B Chat ships a 4K-token context window, while Llama 3 70B Instruct ships a 8K-token context window. On HumanEval, Llama 3 70B Instruct leads by 13.3 pts. On pricing, Llama 2 13B Chat costs $0.1/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 2 13B Chat is ~300% cheaper at $0.1/1M; pay for Llama 3 70B Instruct only for long-context analysis.
Specs
| Released | 2023-07-18 | 2024-04-18 |
| Context window | 4K | 8K |
| Parameters | 13B | 70B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 2 13B Chat | Llama 3 70B Instruct | |
|---|---|---|
| Input price | $0.1/1M tokens | $0.4/1M tokens |
| Output price | $0.5/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Llama 2 13B Chat | Llama 3 70B Instruct | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 2 13B Chat | Llama 3 70B Instruct |
|---|---|---|
| HumanEval | 59.3 | 72.6 |
| Massive Multitask Language Understanding | 71.2 | 82.0 |
Deep dive
On shared benchmark coverage, HumanEval has Llama 2 13B Chat at 59.3 and Llama 3 70B Instruct at 72.6, with Llama 3 70B Instruct ahead by 13.3 points; Massive Multitask Language Understanding has Llama 2 13B Chat at 71.2 and Llama 3 70B Instruct at 82, with Llama 3 70B Instruct ahead by 10.8 points. The largest visible gap is 13.3 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 2 13B Chat lists $0.1/1M input and $0.5/1M output tokens, while Llama 3 70B Instruct lists $0.4/1M input and $0.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 2 13B Chat lower by about $0.18 per million blended tokens. Availability is 12 providers versus 18, so concentration risk also matters.
Choose Llama 2 13B Chat when provider fit and lower input-token cost are central to the workload. Choose Llama 3 70B Instruct when long-context analysis, larger context windows, 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 2 13B Chat or Llama 3 70B Instruct?
Llama 3 70B Instruct supports 8K tokens, while Llama 2 13B Chat supports 4K 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 2 13B Chat or Llama 3 70B Instruct?
Llama 2 13B Chat is cheaper on tracked token pricing. Llama 2 13B Chat costs $0.1/1M input and $0.5/1M output tokens. Llama 3 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 2 13B Chat or Llama 3 70B Instruct open source?
Llama 2 13B Chat is listed under Open Source. Llama 3 70B 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 2 13B Chat or Llama 3 70B Instruct?
Both Llama 2 13B Chat 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 2 13B Chat and Llama 3 70B Instruct?
Llama 2 13B Chat is available on Alibaba Cloud PAI-EAS, AWS Bedrock, Microsoft Foundry, GCP Vertex AI, and Cloudflare Workers 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 2 13B Chat over Llama 3 70B Instruct?
Llama 2 13B Chat is ~300% cheaper at $0.1/1M; pay for Llama 3 70B Instruct only for long-context analysis. If your workload also depends on provider fit, start with Llama 2 13B Chat; if it depends on long-context analysis, run the same evaluation with Llama 3 70B Instruct.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.