Llama Guard 4 12B vs Qwen3.5-9B
Llama Guard 4 12B (2025) and Qwen3.5-9B (2026) are general-purpose language models from AI at Meta and Alibaba. Llama Guard 4 12B ships a 164k-token context window, while Qwen3.5-9B ships a 262k-token context window. On pricing, Qwen3.5-9B costs $0.10/1M input tokens versus $0.18/1M 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.
Qwen3.5-9B is ~80% cheaper at $0.10/1M; pay for Llama Guard 4 12B only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama Guard 4 12B | Qwen3.5-9B |
|---|---|---|
| Best for | provider-routed production | multimodal apps, tool-calling agents, and provider-routed production |
| Decision fit | RAG, Long context, and Classification | Coding, RAG, and Agents |
| Context window | 164k | 262k |
| Cheapest output | $0.18/1M tokens | $0.15/1M tokens |
| Provider routes | 3 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Llama Guard 4 12B for RAG, Long context, and Classification.
- 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 Guard 4 12B
$189
Cheapest tracked route/tier: OpenRouter
Qwen3.5-9B
$118
Cheapest tracked route/tier: Together AI
Estimated monthly gap: $71.50. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Qwen3.5-9B is $0.03/1M 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 OpenRouter; start route-level A/B tests there.
- Llama Guard 4 12B is $0.03/1M 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 | 2025-04-05 | 2026-03-02 |
| Context window | 164k | 262k |
| Parameters | 12B | 9B |
| Architecture | decoder only | decoder only |
| License | Llama 2 Community | Apache 2.0(OSI) |
| Openness | Open weights | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2024-08 | - |
Pricing and availability
| Pricing attribute | Llama Guard 4 12B | Qwen3.5-9B |
|---|---|---|
| Input price | $0.18/1M tokens | $0.10/1M tokens |
| Output price | $0.18/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Llama Guard 4 12B | 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 rows are currently sourced 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 Guard 4 12B lists $0.18/1M input and $0.18/1M 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 $0.07 per million blended tokens. Availability is 3 providers versus 3, so concentration risk also matters.
Choose Llama Guard 4 12B when provider fit are central to the workload. Choose Qwen3.5-9B when long-context analysis, larger context windows, and lower input-token cost 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 Guard 4 12B or Qwen3.5-9B?
Qwen3.5-9B supports 262k tokens, while Llama Guard 4 12B supports 164k 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 Guard 4 12B or Qwen3.5-9B?
Qwen3.5-9B is cheaper on tracked token pricing. Llama Guard 4 12B costs $0.18/1M input and $0.18/1M 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 Guard 4 12B or Qwen3.5-9B open source?
Llama Guard 4 12B is listed under Llama 2 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 Guard 4 12B 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 Guard 4 12B 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 Guard 4 12B and Qwen3.5-9B?
Llama Guard 4 12B is available on NVIDIA NIM, Replicate API, and OpenRouter. 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: 2026-05-22. Data sourced from public model cards and provider documentation.