Llama 3 8B Instruct vs Qwen3-235B-A22B
Llama 3 8B Instruct (2024) and Qwen3-235B-A22B (2025) are compact production models from AI at Meta and Alibaba. Llama 3 8B 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 42.3 pts. On pricing, Llama 3 8B Instruct costs $0.03/1M input tokens versus $0.09/1M 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.
Llama 3 8B Instruct is ~200% cheaper at $0.03/1M; pay for Qwen3-235B-A22B only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Llama 3 8B Instruct | Qwen3-235B-A22B |
|---|---|---|
| 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 | $0.04/1M tokens | $0.58/1M tokens |
| Provider routes | 17 tracked | 5 tracked |
| Shared benchmarks | 3 rows | MMLU PRO leader |
Decision tradeoffs
- Llama 3 8B Instruct has the lower cheapest tracked output price at $0.04/1M tokens.
- Llama 3 8B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3 8B Instruct for Coding, Classification, and JSON / Tool use.
- Qwen3-235B-A22B holds a shared-benchmark lead on MMLU PRO, ahead by 42.3 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 route or tier on this page.
Llama 3 8B Instruct
$34.00
Cheapest tracked route/tier: OpenRouter
Qwen3-235B-A22B
$217
Cheapest tracked route/tier: Novita AI
Estimated monthly gap: $183. Batch, cache, alternate speed tiers, 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 $0.54/1M 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 8B Instruct is $0.54/1M 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 | 8B | 235B |
| 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 | 2023-03 | - |
Pricing and availability
| Pricing attribute | Llama 3 8B Instruct | Qwen3-235B-A22B |
|---|---|---|
| Input price | $0.03/1M tokens | $0.09/1M tokens |
| Output price | $0.04/1M tokens | $0.58/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3 8B 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 |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Llama 3 8B Instruct | Qwen3-235B-A22B |
|---|---|---|
| MMLU PRO | 40.5 | 82.8 |
| Google-Proof Q&A | 44.8 | 86.1 |
| HumanEval | 68.2 | 92.7 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 3 8B Instruct at 40.5 and Qwen3-235B-A22B at 82.8, with Qwen3-235B-A22B ahead by 42.3 points; Google-Proof Q&A has Llama 3 8B Instruct at 44.8 and Qwen3-235B-A22B at 86.1, with Qwen3-235B-A22B ahead by 41.3 points; HumanEval has Llama 3 8B Instruct at 68.2 and Qwen3-235B-A22B at 92.7, with Qwen3-235B-A22B ahead by 24.5 points. The largest visible gap is 42.3 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 8B Instruct lists $0.03/1M input and $0.04/1M output tokens on the cheapest tracked provider, while Qwen3-235B-A22B lists $0.09/1M input and $0.58/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 8B Instruct lower by about $0.20 per million blended tokens. Availability is 17 providers versus 5, so concentration risk also matters.
Choose Llama 3 8B Instruct when provider fit, lower input-token cost, 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 8B Instruct or Qwen3-235B-A22B?
Qwen3-235B-A22B supports 128k tokens, while Llama 3 8B 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 8B Instruct or Qwen3-235B-A22B?
Llama 3 8B Instruct is cheaper on tracked token pricing. Llama 3 8B Instruct costs $0.03/1M input and $0.04/1M output tokens. Qwen3-235B-A22B costs $0.09/1M input and $0.58/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3 8B Instruct or Qwen3-235B-A22B open source?
Llama 3 8B Instruct is listed under Llama 3 Community. 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 8B Instruct or Qwen3-235B-A22B?
Both Llama 3 8B 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 8B Instruct and Qwen3-235B-A22B?
Llama 3 8B Instruct is available on AWS Bedrock, DeepInfra, OctoAI API (Deprecated), Fireworks AI, and Alibaba Cloud PAI-EAS. Qwen3-235B-A22B is available on Fireworks AI, AWS Bedrock, OpenRouter, Venice AI, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3 8B Instruct over Qwen3-235B-A22B?
Llama 3 8B Instruct is ~200% cheaper at $0.03/1M; pay for Qwen3-235B-A22B only for long-context analysis. If your workload also depends on provider fit, start with Llama 3 8B Instruct; if it depends on long-context analysis, run the same evaluation with Qwen3-235B-A22B.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.