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Llama 3 8B Instruct vs Qwen3-9B

Llama 3 8B Instruct (2024) and Qwen3-9B (2026) are compact production models from AI at Meta and Alibaba. Llama 3 8B Instruct ships a 8K-token context window, while Qwen3-9B ships a 256K-token context window. On pricing, Llama 3 8B Instruct costs $0.03/1M input tokens versus $0.04/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Qwen3-9B fits 32x more tokens; pick it for long-context work and Llama 3 8B Instruct for tighter calls.

Specs

Released2024-04-182026-03-02
Context window8K256K
Parameters8B9B
Architecturedecoder onlydecoder only
LicenseOpen SourceApache 2.0
Knowledge cutoff--

Pricing and availability

Llama 3 8B InstructQwen3-9B
Input price$0.03/1M tokens$0.04/1M tokens
Output price$0.04/1M tokens$0.2/1M tokens
Providers

Capabilities

Llama 3 8B InstructQwen3-9B
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

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, while Qwen3-9B lists $0.04/1M input and $0.2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 8B Instruct lower by about $0.05 per million blended tokens. Availability is 17 providers versus 1, 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 Qwen3-9B 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.

FAQ

Which has a larger context window, Llama 3 8B Instruct or Qwen3-9B?

Qwen3-9B supports 256K 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 Qwen3-9B?

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. Qwen3-9B costs $0.04/1M input and $0.2/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Llama 3 8B Instruct or Qwen3-9B open source?

Llama 3 8B Instruct is listed under Open Source. Qwen3-9B 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 Qwen3-9B?

Both Llama 3 8B Instruct and Qwen3-9B 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 Qwen3-9B?

Llama 3 8B Instruct is available on AWS Bedrock, DeepInfra, OctoAI API, Fireworks AI, and Alibaba Cloud PAI-EAS. Qwen3-9B is available on DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Llama 3 8B Instruct over Qwen3-9B?

Qwen3-9B fits 32x more tokens; pick it for long-context work and Llama 3 8B Instruct for tighter calls. 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 Qwen3-9B.

Continue comparing

Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.