Llama 3.1 405B Instruct vs Qwen1.5-110B
Llama 3.1 405B Instruct (2024) and Qwen1.5-110B (2024) are compact production models from AI at Meta and Alibaba. Llama 3.1 405B Instruct ships a 128K-token context window, while Qwen1.5-110B ships a 32K-token context window. On Massive Multitask Language Understanding, Llama 3.1 405B Instruct leads by 10.4 pts. On pricing, Qwen1.5-110B costs $1.5/1M input tokens versus $2.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Qwen1.5-110B is ~60% cheaper at $1.5/1M; pay for Llama 3.1 405B Instruct only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Llama 3.1 405B Instruct | Qwen1.5-110B |
|---|---|---|
| Decision fit | RAG, Long context, and Classification | Coding, Classification, and JSON / Tool use |
| Context window | 128K | 32K |
| Cheapest output | $2.4/1M tokens | $2.5/1M tokens |
| Provider routes | 11 tracked | 2 tracked |
| Shared benchmarks | Massive Multitask Language Understanding leader | 1 rows |
Decision tradeoffs
- Llama 3.1 405B Instruct leads the largest shared benchmark signal on Massive Multitask Language Understanding by 10.4 points.
- Llama 3.1 405B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.1 405B Instruct has the lower cheapest tracked output price at $2.4/1M tokens.
- Llama 3.1 405B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3.1 405B Instruct for RAG, Long context, and Classification.
- Local decision data tags Qwen1.5-110B for Coding, 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 Instruct
$2,520
Cheapest tracked route: AWS Bedrock
Qwen1.5-110B
$1,825
Cheapest tracked route: Microsoft Foundry
Estimated monthly gap: $695. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Microsoft Foundry and Together AI; start route-level A/B tests there.
- Qwen1.5-110B is $0.1/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on Together AI and Microsoft Foundry; start route-level A/B tests there.
- Llama 3.1 405B Instruct is $0.1/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2024-04-25 |
| Context window | 128K | 32K |
| Parameters | 405B | 110B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Llama 3.1 405B Instruct | Qwen1.5-110B |
|---|---|---|
| Input price | $2.4/1M tokens | $1.5/1M tokens |
| Output price | $2.4/1M tokens | $2.5/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 405B Instruct | Qwen1.5-110B |
|---|---|---|
| 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 |
Benchmarks
| Benchmark | Llama 3.1 405B Instruct | Qwen1.5-110B |
|---|---|---|
| Massive Multitask Language Understanding | 88.6 | 78.2 |
Deep dive
On shared benchmark coverage, Massive Multitask Language Understanding has Llama 3.1 405B Instruct at 88.6 and Qwen1.5-110B at 78.2, with Llama 3.1 405B Instruct ahead by 10.4 points. The largest visible gap is 10.4 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 3.1 405B Instruct lists $2.4/1M input and $2.4/1M output tokens, while Qwen1.5-110B lists $1.5/1M input and $2.5/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen1.5-110B lower by about $0.6 per million blended tokens. Availability is 11 providers versus 2, so concentration risk also matters.
Choose Llama 3.1 405B Instruct when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose Qwen1.5-110B when provider fit 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 3.1 405B Instruct or Qwen1.5-110B?
Llama 3.1 405B Instruct supports 128K tokens, while Qwen1.5-110B supports 32K 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 3.1 405B Instruct or Qwen1.5-110B?
Qwen1.5-110B is cheaper on tracked token pricing. Llama 3.1 405B Instruct costs $2.4/1M input and $2.4/1M output tokens. Qwen1.5-110B costs $1.5/1M input and $2.5/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.1 405B Instruct or Qwen1.5-110B open source?
Llama 3.1 405B Instruct is listed under Open Source. Qwen1.5-110B 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 Instruct or Qwen1.5-110B?
Both Llama 3.1 405B Instruct and Qwen1.5-110B 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 3.1 405B Instruct and Qwen1.5-110B?
Llama 3.1 405B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, IBM watsonx, and Scale AI GenAI Platform. Qwen1.5-110B is available on Microsoft Foundry and Together AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 405B Instruct over Qwen1.5-110B?
Qwen1.5-110B is ~60% cheaper at $1.5/1M; pay for Llama 3.1 405B Instruct only for long-context analysis. If your workload also depends on long-context analysis, start with Llama 3.1 405B Instruct; if it depends on provider fit, run the same evaluation with Qwen1.5-110B.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.