Llama 3 70B Instruct vs Qwen2.5-72B-Instruct
Llama 3 70B Instruct (2024) and Qwen2.5-72B-Instruct (2024) are compact production models from AI at Meta and Alibaba. Llama 3 70B Instruct ships a 8k-token context window, while Qwen2.5-72B-Instruct ships a 128k-token context window. On HumanEval, Qwen2.5-72B-Instruct leads by 14 pts. On pricing, Qwen2.5-72B-Instruct costs $0.18/1M input tokens versus $0.40/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Qwen2.5-72B-Instruct is ~122% cheaper at $0.18/1M; pay for Llama 3 70B Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3 70B Instruct | Qwen2.5-72B-Instruct |
|---|---|---|
| Best for | provider-routed production | provider-routed production |
| Decision fit | Coding, Classification, and JSON / Tool use | Coding, RAG, and Long context |
| Context window | 8k | 128k |
| Cheapest output | $0.40/1M tokens | $0.54/1M tokens |
| Provider routes | 18 tracked | 7 tracked |
| Shared benchmarks | 2 rows | HumanEval leader |
Decision tradeoffs
- 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.
- Qwen2.5-72B-Instruct holds a shared-benchmark lead on HumanEval, ahead by 14 points.
- Qwen2.5-72B-Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Qwen2.5-72B-Instruct for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3 70B Instruct
$420
Cheapest tracked route/tier: Hyperbolic AI Inference
Qwen2.5-72B-Instruct
$279
Cheapest tracked route/tier: Chutes AI
Estimated monthly gap: $141. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on DeepInfra, OpenRouter, and Fireworks AI; start route-level A/B tests there.
- Qwen2.5-72B-Instruct is $0.14/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on DeepInfra, Fireworks AI, and OpenRouter; start route-level A/B tests there.
- Llama 3 70B Instruct is $0.14/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-04-18 | 2024-06-07 |
| Context window | 8k | 128k |
| Parameters | 70B | 72.7B |
| Architecture | decoder only | decoder only |
| License | Llama 3 Community | Apache 2.0(OSI) |
| Openness | Open weights | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Llama 3 70B Instruct | Qwen2.5-72B-Instruct |
|---|---|---|
| Input price | $0.40/1M tokens | $0.18/1M tokens |
| Output price | $0.40/1M tokens | $0.54/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3 70B Instruct | Qwen2.5-72B-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 70B Instruct | Qwen2.5-72B-Instruct |
|---|---|---|
| HumanEval | 72.6 | 86.6 |
| Massive Multitask Language Understanding | 82.0 | 88.2 |
Deep dive
On shared benchmark coverage, HumanEval has Llama 3 70B Instruct at 72.6 and Qwen2.5-72B-Instruct at 86.6, with Qwen2.5-72B-Instruct ahead by 14 points; Massive Multitask Language Understanding has Llama 3 70B Instruct at 82 and Qwen2.5-72B-Instruct at 88.2, with Qwen2.5-72B-Instruct ahead by 6.2 points. The largest visible gap is 14 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 70B Instruct lists $0.40/1M input and $0.40/1M output tokens on the cheapest tracked provider, while Qwen2.5-72B-Instruct lists $0.18/1M input and $0.54/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen2.5-72B-Instruct lower by about $0.11 per million blended tokens. Availability is 18 providers versus 7, so concentration risk also matters.
Choose Llama 3 70B Instruct when provider fit and broader provider choice are central to the workload. Choose Qwen2.5-72B-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 3 70B Instruct or Qwen2.5-72B-Instruct?
Qwen2.5-72B-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 70B Instruct or Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct is cheaper on tracked token pricing. Llama 3 70B Instruct costs $0.40/1M input and $0.40/1M output tokens. Qwen2.5-72B-Instruct costs $0.18/1M input and $0.54/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3 70B Instruct or Qwen2.5-72B-Instruct open source?
Llama 3 70B Instruct is listed under Llama 3 Community. Qwen2.5-72B-Instruct is listed under Apache 2.0. 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 70B Instruct or Qwen2.5-72B-Instruct?
Both Llama 3 70B Instruct and Qwen2.5-72B-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 70B Instruct and Qwen2.5-72B-Instruct?
Llama 3 70B Instruct is available on GCP Vertex AI, AWS Bedrock, Microsoft Foundry, NVIDIA NIM, and DeepInfra. Qwen2.5-72B-Instruct is available on DeepInfra, OpenRouter, Fireworks AI, Novita AI, and Chutes AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3 70B Instruct over Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct is ~122% cheaper at $0.18/1M; pay for Llama 3 70B Instruct only for provider fit. If your workload also depends on provider fit, start with Llama 3 70B Instruct; if it depends on long-context analysis, run the same evaluation with Qwen2.5-72B-Instruct.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.