Llama 2 70B Chat vs Llama 3 70B Instruct
Llama 2 70B Chat (2023) and Llama 3 70B Instruct (2024) are compact production models from AI at Meta. Llama 2 70B Chat ships a 4k-token context window, while Llama 3 70B Instruct ships a 8k-token context window. On Massive Multitask Language Understanding, Llama 3 70B Instruct leads by 13.1 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 70B Instruct is safer overall; choose Llama 2 70B Chat when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 2 70B Chat | Llama 3 70B Instruct |
|---|---|---|
| Best for | provider-routed production | provider-routed production |
| Decision fit | Classification and JSON / Tool use | Coding, Classification, and JSON / Tool use |
| Context window | 4k | 8k |
| Cheapest output | $1.50/1M tokens | $0.40/1M tokens |
| Provider routes | 14 tracked | 18 tracked |
| Shared benchmarks | 1 rows | Massive Multitask Language Understanding leader |
Decision tradeoffs
- Local decision data tags Llama 2 70B Chat for Classification and JSON / Tool use.
- Llama 3 70B Instruct holds a shared-benchmark lead on Massive Multitask Language Understanding, ahead by 13.1 points.
- Llama 3 70B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3 70B Instruct has the lower cheapest tracked output price at $0.40/1M tokens.
- 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 2 70B Chat
$775
Cheapest tracked route/tier: Databricks Foundation Model Serving
Llama 3 70B Instruct
$420
Cheapest tracked route/tier: Hyperbolic AI Inference
Estimated monthly gap: $355. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on GCP Vertex AI, AWS Bedrock, and Microsoft Foundry; start route-level A/B tests there.
- Llama 3 70B Instruct is $1.10/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Provider overlap exists on Databricks Foundation Model Serving, Microsoft Foundry, and GCP Vertex AI; start route-level A/B tests there.
- Llama 2 70B Chat is $1.10/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-07-18 | 2024-04-18 |
| Context window | 4k | 8k |
| Parameters | 70B | 70B |
| Architecture | decoder only | decoder only |
| License | Llama 2 Community | Llama 3 Community |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Pricing attribute | Llama 2 70B Chat | Llama 3 70B Instruct |
|---|---|---|
| Input price | $0.50/1M tokens | $0.40/1M tokens |
| Output price | $1.50/1M tokens | $0.40/1M tokens |
| Providers |
Capabilities
| Capability | Llama 2 70B Chat | 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 2 70B Chat | Llama 3 70B Instruct |
|---|---|---|
| Massive Multitask Language Understanding | 68.9 | 82.0 |
Deep dive
On shared benchmark coverage, Massive Multitask Language Understanding has Llama 2 70B Chat at 68.9 and Llama 3 70B Instruct at 82, with Llama 3 70B Instruct ahead by 13.1 points. The largest visible gap is 13.1 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 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 70B Chat lists $0.50/1M input and $1.50/1M 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 70B Instruct lower by about $0.40 per million blended tokens. Availability is 14 providers versus 18, so concentration risk also matters.
Choose Llama 2 70B Chat when provider fit are central to the workload. Choose Llama 3 70B Instruct when long-context analysis, larger context windows, 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, Llama 2 70B Chat or Llama 3 70B Instruct?
Llama 3 70B Instruct supports 8k tokens, while Llama 2 70B 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 70B Chat or Llama 3 70B Instruct?
Llama 3 70B Instruct is cheaper on tracked token pricing. Llama 2 70B Chat costs $0.50/1M input and $1.50/1M 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 2 70B Chat or Llama 3 70B Instruct open source?
Llama 2 70B Chat is listed under Llama 2 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 2 70B Chat or Llama 3 70B Instruct?
Both Llama 2 70B 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 70B Chat and Llama 3 70B Instruct?
Llama 2 70B Chat is available on Databricks Foundation Model Serving, Microsoft Foundry, GCP Vertex AI, Alibaba Cloud PAI-EAS, and AWS Bedrock. 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 70B Chat over Llama 3 70B Instruct?
Llama 3 70B Instruct is safer overall; choose Llama 2 70B Chat when provider fit matters. If your workload also depends on provider fit, start with Llama 2 70B Chat; if it depends on long-context analysis, run the same evaluation with Llama 3 70B Instruct.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.