Llama 3.1 8B Instruct vs Qwen3.5-9B
Llama 3.1 8B Instruct (2024) and Qwen3.5-9B (2026) are compact production models from AI at Meta and Alibaba. Llama 3.1 8B Instruct ships a 128K-token context window, while Qwen3.5-9B ships a 262K-token context window. On MMLU PRO, Qwen3.5-9B leads by 38.3 pts. On pricing, Llama 3.1 8B Instruct costs $0.02/1M input tokens versus $0.1/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.1 8B Instruct is ~400% cheaper at $0.02/1M; pay for Qwen3.5-9B only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Llama 3.1 8B Instruct | Qwen3.5-9B |
|---|---|---|
| Decision fit | RAG, Long context, and Classification | RAG, Agents, and Long context |
| Context window | 128K | 262K |
| Cheapest output | $0.05/1M tokens | $0.15/1M tokens |
| Provider routes | 12 tracked | 3 tracked |
| Shared benchmarks | 1 rows | MMLU PRO leader |
Decision tradeoffs
- Llama 3.1 8B Instruct has the lower cheapest tracked output price at $0.05/1M tokens.
- Llama 3.1 8B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3.1 8B Instruct for RAG, Long context, and Classification.
- Qwen3.5-9B leads the largest shared benchmark signal on MMLU PRO by 38.3 points.
- Qwen3.5-9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-9B uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Qwen3.5-9B for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.1 8B Instruct
$28.50
Cheapest tracked route: OpenRouter
Qwen3.5-9B
$118
Cheapest tracked route: Together AI
Estimated monthly gap: $89.00. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Together AI and OpenRouter; start route-level A/B tests there.
- Qwen3.5-9B is $0.1/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Qwen3.5-9B adds Vision, Multimodal, and Function calling in local capability data.
- Provider overlap exists on Together AI and OpenRouter; start route-level A/B tests there.
- Llama 3.1 8B Instruct is $0.1/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2026-03-02 |
| Context window | 128K | 262K |
| Parameters | 8B | 9B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 8B Instruct | Qwen3.5-9B |
|---|---|---|
| Input price | $0.02/1M tokens | $0.1/1M tokens |
| Output price | $0.05/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 8B Instruct | Qwen3.5-9B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Llama 3.1 8B Instruct | Qwen3.5-9B |
|---|---|---|
| MMLU PRO | 44.3 | 82.5 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 3.1 8B Instruct at 44.3 and Qwen3.5-9B at 82.5, with Qwen3.5-9B ahead by 38.3 points. The largest visible gap is 38.3 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 differs most on vision: Qwen3.5-9B, multimodal input: Qwen3.5-9B, function calling: Qwen3.5-9B, and tool use: Qwen3.5-9B. Both models share structured outputs, 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.
For cost, Llama 3.1 8B Instruct lists $0.02/1M input and $0.05/1M output tokens, while Qwen3.5-9B lists $0.1/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 8B Instruct lower by about $0.09 per million blended tokens. Availability is 12 providers versus 3, so concentration risk also matters.
Choose Llama 3.1 8B Instruct when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Qwen3.5-9B 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.
FAQ
Which has a larger context window, Llama 3.1 8B Instruct or Qwen3.5-9B?
Qwen3.5-9B supports 262K tokens, while Llama 3.1 8B Instruct supports 128K 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 8B Instruct or Qwen3.5-9B?
Llama 3.1 8B Instruct is cheaper on tracked token pricing. Llama 3.1 8B Instruct costs $0.02/1M input and $0.05/1M output tokens. Qwen3.5-9B costs $0.1/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.1 8B Instruct or Qwen3.5-9B open source?
Llama 3.1 8B Instruct is listed under Open Source. Qwen3.5-9B 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 vision, Llama 3.1 8B Instruct or Qwen3.5-9B?
Qwen3.5-9B has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Llama 3.1 8B Instruct or Qwen3.5-9B?
Qwen3.5-9B has the clearer documented multimodal input signal in this comparison. If multimodal input 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 8B Instruct and Qwen3.5-9B?
Llama 3.1 8B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, NVIDIA NIM, and GroqCloud. Qwen3.5-9B is available on Together AI, OpenRouter, and Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.