Llama 3 70B Instruct vs Qwen3-235B-A22B
Llama 3 70B Instruct (2024) and Qwen3-235B-A22B (2025) are compact production models from AI at Meta and Alibaba. Llama 3 70B Instruct ships a 8K-token context window, while Qwen3-235B-A22B ships a 128K-token context window. On MMLU PRO, Qwen3-235B-A22B leads by 25.4 pts. On pricing, Llama 3 70B Instruct costs $0.4/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Pick Qwen3-235B-A22B for general evaluation; Llama 3 70B Instruct is better when provider fit matters more.
Decision scorecard
Local evidence first| Signal | Llama 3 70B Instruct | Qwen3-235B-A22B |
|---|---|---|
| Decision fit | Coding, Classification, and JSON / Tool use | Coding, RAG, and Long context |
| Context window | 8K | 128K |
| Cheapest output | $0.4/1M tokens | $1.2/1M tokens |
| Provider routes | 17 tracked | 4 tracked |
| Shared benchmarks | 2 rows | MMLU PRO leader |
Decision tradeoffs
- Llama 3 70B Instruct has the lower cheapest tracked output price at $0.4/1M 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.
- Qwen3-235B-A22B leads the largest shared benchmark signal on MMLU PRO by 25.4 points.
- Qwen3-235B-A22B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Qwen3-235B-A22B for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3 70B Instruct
$420
Cheapest tracked route: Hyperbolic AI Inference
Qwen3-235B-A22B
$620
Cheapest tracked route: AWS Bedrock
Estimated monthly gap: $200. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI, AWS Bedrock, and OpenRouter; start route-level A/B tests there.
- Qwen3-235B-A22B is $0.8/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on AWS Bedrock, Fireworks AI, and OpenRouter; start route-level A/B tests there.
- Llama 3 70B Instruct is $0.8/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-04-18 | 2025-04-29 |
| Context window | 8K | 128K |
| Parameters | 70B | 235B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Llama 3 70B Instruct | Qwen3-235B-A22B |
|---|---|---|
| Input price | $0.4/1M tokens | $0.4/1M tokens |
| Output price | $0.4/1M tokens | $1.2/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3 70B Instruct | Qwen3-235B-A22B |
|---|---|---|
| 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 70B Instruct | Qwen3-235B-A22B |
|---|---|---|
| MMLU PRO | 57.4 | 82.8 |
| HumanEval | 72.6 | 92.7 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 3 70B Instruct at 57.4 and Qwen3-235B-A22B at 82.8, with Qwen3-235B-A22B ahead by 25.4 points; HumanEval has Llama 3 70B Instruct at 72.6 and Qwen3-235B-A22B at 92.7, with Qwen3-235B-A22B ahead by 20.1 points. The largest visible gap is 25.4 points on MMLU PRO, 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 $0.4/1M input and $0.4/1M output tokens, while Qwen3-235B-A22B lists $0.4/1M input and $1.2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 70B Instruct lower by about $0.24 per million blended tokens. Availability is 17 providers versus 4, so concentration risk also matters.
Choose Llama 3 70B Instruct when provider fit and broader provider choice are central to the workload. Choose Qwen3-235B-A22B when long-context analysis and larger context windows 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 Qwen3-235B-A22B?
Qwen3-235B-A22B 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 Qwen3-235B-A22B?
Llama 3 70B Instruct is cheaper on tracked token pricing. Llama 3 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Qwen3-235B-A22B costs $0.4/1M input and $1.2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3 70B Instruct or Qwen3-235B-A22B open source?
Llama 3 70B Instruct is listed under Open Source. Qwen3-235B-A22B 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 Qwen3-235B-A22B?
Both Llama 3 70B Instruct and Qwen3-235B-A22B 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 Qwen3-235B-A22B?
Llama 3 70B Instruct is available on GCP Vertex AI, AWS Bedrock, Microsoft Foundry, NVIDIA NIM, and DeepInfra. Qwen3-235B-A22B is available on Fireworks AI, AWS Bedrock, OpenRouter, and Venice AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3 70B Instruct over Qwen3-235B-A22B?
Pick Qwen3-235B-A22B for general evaluation; Llama 3 70B Instruct is better when provider fit matters more. 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 Qwen3-235B-A22B.
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Last reviewed: 2026-05-20. Data sourced from public model cards and provider documentation.