Llama 3.1 405B vs Qwen1.5-72B
Llama 3.1 405B (2024) and Qwen1.5-72B (2024) are compact production models from AI at Meta and Alibaba. Llama 3.1 405B ships a 128K-token context window, while Qwen1.5-72B ships a 32K-token context window. On Massive Multitask Language Understanding, Llama 3.1 405B leads by 8.8 pts. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Llama 3.1 405B fits 4x more tokens; pick it for long-context work and Qwen1.5-72B for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.1 405B | Qwen1.5-72B |
|---|---|---|
| Decision fit | Coding, Long context, and Classification | Classification and JSON / Tool use |
| Context window | 128K | 32K |
| Cheapest output | - | $2.75/1M tokens |
| Provider routes | 0 tracked | 4 tracked |
| Shared benchmarks | Massive Multitask Language Understanding leader | 1 rows |
Decision tradeoffs
- Llama 3.1 405B leads the largest shared benchmark signal on Massive Multitask Language Understanding by 8.8 points.
- Llama 3.1 405B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Llama 3.1 405B for Coding, Long context, and Classification.
- Qwen1.5-72B has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen1.5-72B uniquely exposes Structured outputs in local model data.
- Local decision data tags Qwen1.5-72B for Classification and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.1 405B
Unavailable
No complete token price in local provider data
Qwen1.5-72B
$1,208
Cheapest tracked route: Replicate API
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.1 405B and Qwen1.5-72B; plan for SDK, billing, or endpoint changes.
- Qwen1.5-72B adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Qwen1.5-72B and Llama 3.1 405B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2024-02-05 |
| Context window | 128K | 32K |
| Parameters | 405B | 72B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Llama 3.1 405B | Qwen1.5-72B |
|---|---|---|
| Input price | - | $0.65/1M tokens |
| Output price | - | $2.75/1M tokens |
| Providers | - |
Capabilities
| Capability | Llama 3.1 405B | Qwen1.5-72B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Llama 3.1 405B | Qwen1.5-72B |
|---|---|---|
| Massive Multitask Language Understanding | 88.6 | 79.8 |
Deep dive
On shared benchmark coverage, Massive Multitask Language Understanding has Llama 3.1 405B at 88.6 and Qwen1.5-72B at 79.8, with Llama 3.1 405B ahead by 8.8 points. The largest visible gap is 8.8 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 differs most on structured outputs: Qwen1.5-72B. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: Llama 3.1 405B has no token price sourced yet and Qwen1.5-72B has $0.65/1M input tokens. Provider availability is 0 tracked routes versus 4. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 405B when long-context analysis and larger context windows are central to the workload. Choose Qwen1.5-72B 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 3.1 405B or Qwen1.5-72B?
Llama 3.1 405B supports 128K tokens, while Qwen1.5-72B supports 32K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.1 405B or Qwen1.5-72B open source?
Llama 3.1 405B is listed under Open Source. Qwen1.5-72B 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 3.1 405B or Qwen1.5-72B?
Qwen1.5-72B has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Llama 3.1 405B and Qwen1.5-72B?
Llama 3.1 405B is available on the tracked providers still being sourced. Qwen1.5-72B is available on Alibaba Cloud PAI-EAS, Fireworks AI, Together AI, and Replicate API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 405B over Qwen1.5-72B?
Llama 3.1 405B fits 4x more tokens; pick it for long-context work and Qwen1.5-72B for tighter calls. If your workload also depends on long-context analysis, start with Llama 3.1 405B; if it depends on provider fit, run the same evaluation with Qwen1.5-72B.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.