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Together AI - Qwen3.5-9B vs ShieldGemma 9B

Together AI - Qwen3.5-9B (2026) and ShieldGemma 9B (2024) are compact production models from Alibaba and Google DeepMind. Together AI - Qwen3.5-9B ships a 33K-token context window, while ShieldGemma 9B ships a 8K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.

Together AI - Qwen3.5-9B fits 4x more tokens; pick it for long-context work and ShieldGemma 9B for tighter calls.

Specs

Released2026-02-012024-07-01
Context window33K8K
Parameters9B9B
Architecturedecoder onlydecoder only
LicenseOpen Source1
Knowledge cutoff--

Pricing and availability

Together AI - Qwen3.5-9BShieldGemma 9B
Input price$0.1/1M tokens-
Output price$0.15/1M tokens-
Providers

Capabilities

Together AI - Qwen3.5-9BShieldGemma 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 differs most on function calling: Together AI - Qwen3.5-9B, tool use: Together AI - Qwen3.5-9B, and structured outputs: Together AI - Qwen3.5-9B. Both models share the core language-model surface, 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.

Pricing coverage is uneven: Together AI - Qwen3.5-9B has $0.1/1M input tokens and ShieldGemma 9B has no token price sourced yet. Provider availability is 1 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Together AI - Qwen3.5-9B when long-context analysis and larger context windows are central to the workload. Choose ShieldGemma 9B when provider fit 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, Together AI - Qwen3.5-9B or ShieldGemma 9B?

Together AI - Qwen3.5-9B supports 33K tokens, while ShieldGemma 9B supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is Together AI - Qwen3.5-9B or ShieldGemma 9B open source?

Together AI - Qwen3.5-9B is listed under Open Source. ShieldGemma 9B is listed under 1. 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 function calling, Together AI - Qwen3.5-9B or ShieldGemma 9B?

Together AI - Qwen3.5-9B has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for tool use, Together AI - Qwen3.5-9B or ShieldGemma 9B?

Together AI - Qwen3.5-9B has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for structured outputs, Together AI - Qwen3.5-9B or ShieldGemma 9B?

Together AI - Qwen3.5-9B has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Together AI - Qwen3.5-9B and ShieldGemma 9B?

Together AI - Qwen3.5-9B is available on Together AI. ShieldGemma 9B is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Continue comparing

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