LLM Reference

Gemini 1.5 Flash vs Qwen3.5-9B

Gemini 1.5 Flash (2024) and Qwen3.5-9B (2026) are general-purpose language models from Google DeepMind and Alibaba. Gemini 1.5 Flash ships a 1m-token context window, while Qwen3.5-9B ships a 262k-token context window. On MMLU PRO, Qwen3.5-9B leads by 23.4 pts. On pricing, Gemini 1.5 Flash costs $0.07/1M input tokens versus $0.10/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Qwen3.5-9B is safer overall; choose Gemini 1.5 Flash when long-context analysis matters.

Decision scorecard

Local evidence first
SignalGemini 1.5 FlashQwen3.5-9B
Best forlong-context analysis and provider-routed productionmultimodal apps, tool-calling agents, and provider-routed production
Decision fitRAG, Long context, and ClassificationRAG, Agents, and Long context
Context window1m262k
Cheapest output$0.30/1M tokens$0.15/1M tokens
Provider routes2 tracked3 tracked
Shared benchmarks1 rowsMMLU PRO leader

Decision tradeoffs

Choose Gemini 1.5 Flash when...
  • Gemini 1.5 Flash has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Local decision data tags Gemini 1.5 Flash for RAG, Long context, and Classification.
Choose Qwen3.5-9B when...
  • Qwen3.5-9B leads the largest shared benchmark signal on MMLU PRO by 23.4 points.
  • Qwen3.5-9B has the lower cheapest tracked output price at $0.15/1M tokens.
  • Qwen3.5-9B has broader tracked provider coverage for fallback and procurement flexibility.
  • 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 route or tier on this page.

Lower estimate Qwen3.5-9B

Gemini 1.5 Flash

$135

Cheapest tracked route/tier: GCP Vertex AI

Qwen3.5-9B

$118

Cheapest tracked route/tier: Together AI

Estimated monthly gap: $17.50. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.

Switch friction

Gemini 1.5 Flash -> Qwen3.5-9B
  • No overlapping tracked provider route is sourced for Gemini 1.5 Flash and Qwen3.5-9B; plan for SDK, billing, or endpoint changes.
  • Qwen3.5-9B is $0.15/1M 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.
Qwen3.5-9B -> Gemini 1.5 Flash
  • No overlapping tracked provider route is sourced for Qwen3.5-9B and Gemini 1.5 Flash; plan for SDK, billing, or endpoint changes.
  • Gemini 1.5 Flash is $0.15/1M 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
Released2024-05-142026-03-02
Context window1m262k
Parameters9B
Architecturedecoder onlydecoder only
LicenseUnknownApache 2.0
Knowledge cutoff2024-05-

Pricing and availability

Pricing attributeGemini 1.5 FlashQwen3.5-9B
Input price$0.07/1M tokens$0.10/1M tokens
Output price$0.30/1M tokens$0.15/1M tokens
Providers

Capabilities

CapabilityGemini 1.5 FlashQwen3.5-9B
VisionNoYes
MultimodalNoYes
ReasoningNoNo
Function callingNoYes
Tool useNoYes
Structured outputsYesYes
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

BenchmarkGemini 1.5 FlashQwen3.5-9B
MMLU PRO59.182.5

Deep dive

On shared benchmark coverage, MMLU PRO has Gemini 1.5 Flash at 59.1 and Qwen3.5-9B at 82.5, with Qwen3.5-9B ahead by 23.4 points. The largest visible gap is 23.4 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, Gemini 1.5 Flash lists $0.07/1M input and $0.30/1M 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 $0.03 per million blended tokens. Availability is 2 providers versus 3, so concentration risk also matters.

Choose Gemini 1.5 Flash when long-context analysis, larger context windows, and lower input-token cost are central to the workload. Choose Qwen3.5-9B when vision-heavy evaluation and broader provider choice 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, Gemini 1.5 Flash or Qwen3.5-9B?

Gemini 1.5 Flash supports 1m tokens, while Qwen3.5-9B supports 262k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Which is cheaper, Gemini 1.5 Flash or Qwen3.5-9B?

Qwen3.5-9B is cheaper on tracked token pricing. Gemini 1.5 Flash costs $0.07/1M input and $0.30/1M 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 Gemini 1.5 Flash or Qwen3.5-9B open source?

Gemini 1.5 Flash is listed under Unknown. 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, Gemini 1.5 Flash 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, Gemini 1.5 Flash 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 Gemini 1.5 Flash and Qwen3.5-9B?

Gemini 1.5 Flash is available on GCP Vertex AI and Google AI Studio. 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: 2026-05-19. Data sourced from public model cards and provider documentation.