Llama 3.3 70B Instruct (free) vs Qwen3.5-9B
Llama 3.3 70B Instruct (free) (2024) and Qwen3.5-9B (2026) are compact production models from AI at Meta and Alibaba. Llama 3.3 70B Instruct (free) ships a 66k-token context window, while Qwen3.5-9B ships a 262k-token context window. On pricing, both list $0.10/1M input tokens on the cheapest tracked route. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Qwen3.5-9B is safer overall; choose Llama 3.3 70B Instruct (free) when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.3 70B Instruct (free) | Qwen3.5-9B |
|---|---|---|
| Best for | provider-routed production | multimodal apps, tool-calling agents, and provider-routed production |
| Decision fit | Classification and JSON / Tool use | Coding, RAG, and Agents |
| Context window | 66k | 262k |
| Cheapest output | $0.32/1M tokens | $0.15/1M tokens |
| Provider routes | 11 tracked | 3 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- Llama 3.3 70B Instruct (free) has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3.3 70B Instruct (free) for Classification and JSON / Tool use.
- Qwen3.5-9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-9B has the lower cheapest tracked output price at $0.15/1M tokens.
- Qwen3.5-9B uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Qwen3.5-9B for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.3 70B Instruct (free)
$160
Cheapest tracked route/tier: OpenRouter
Qwen3.5-9B
$118
Cheapest tracked route/tier: Together AI
Estimated monthly gap: $42.50. Batch, cache, alternate speed tiers, 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 $0.17/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- 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 3.3 70B Instruct (free) is $0.17/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-06 | 2026-03-02 |
| Context window | 66k | 262k |
| Parameters | 70B | 9B |
| Architecture | Decoder Only | Decoder Only |
| License | Llama 3 Community | Apache 2.0OSI-approved |
| Openness | Open weights | Open source |
| Commercial use | Commercial use: conditional | Commercial use: permitted |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Llama 3.3 70B Instruct (free) | Qwen3.5-9B |
|---|---|---|
| Input price | $0.10/1M tokens | $0.10/1M tokens |
| Output price | $0.32/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.3 70B Instruct (free) | 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 |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark scores are currently available for this pair.
Deep dive
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 3.3 70B Instruct (free) lists $0.10/1M input and $0.32/1M output tokens on the cheapest tracked provider, while Qwen3.5-9B lists $0.10/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-9B lower by about $0.05 per million blended tokens. Availability is 11 providers versus 3, so concentration risk also matters.
Choose Llama 3.3 70B Instruct (free) when provider fit 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. 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 3.3 70B Instruct (free) or Qwen3.5-9B?
Qwen3.5-9B supports 262k tokens, while Llama 3.3 70B Instruct (free) supports 66k 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 3.3 70B Instruct (free) or Qwen3.5-9B?
Qwen3.5-9B is cheaper on tracked token pricing. Llama 3.3 70B Instruct (free) costs $0.10/1M input and $0.32/1M output tokens. Qwen3.5-9B costs $0.10/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.3 70B Instruct (free) or Qwen3.5-9B open source?
Llama 3.3 70B Instruct (free) is listed under Llama 3 Community. 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 3.3 70B Instruct (free) 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 3.3 70B Instruct (free) 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 3.3 70B Instruct (free) and Qwen3.5-9B?
Llama 3.3 70B Instruct (free) is available on Cloudflare Workers AI, NVIDIA NIM, GroqCloud, Together AI, and Arcee AI. 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: 2026-06-15. Data sourced from public model cards and provider documentation.