gpt-realtime vs Qwen3.5-9B
gpt-realtime (2025) and Qwen3.5-9B (2026) are compact production models from OpenAI and Alibaba. gpt-realtime ships a 32K-token context window, while Qwen3.5-9B ships a 262K-token context window. On pricing, Qwen3.5-9B costs $0.1/1M input tokens versus $4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3.5-9B is ~3900% cheaper at $0.1/1M; pay for gpt-realtime only for provider fit.
Decision scorecard
Local evidence first| Signal | gpt-realtime | Qwen3.5-9B |
|---|---|---|
| Decision fit | Vision | RAG, Agents, and Long context |
| Context window | 32K | 262K |
| Cheapest output | $16/1M tokens | $0.15/1M tokens |
| Provider routes | 1 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags gpt-realtime for Vision.
- 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 has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen3.5-9B uniquely exposes Vision, Function calling, and Tool use 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.
gpt-realtime
$7,200
Cheapest tracked route: OpenAI API
Qwen3.5-9B
$118
Cheapest tracked route: Together AI
Estimated monthly gap: $7,083. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- No overlapping tracked provider route is sourced for gpt-realtime and Qwen3.5-9B; plan for SDK, billing, or endpoint changes.
- Qwen3.5-9B is $15.85/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Qwen3.5-9B adds Vision, Function calling, and Tool use in local capability data.
- No overlapping tracked provider route is sourced for Qwen3.5-9B and gpt-realtime; plan for SDK, billing, or endpoint changes.
- gpt-realtime is $15.85/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision, Function calling, and Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-10-06 | 2026-03-02 |
| Context window | 32K | 262K |
| Parameters | — | 9B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | 2023-10 | - |
Pricing and availability
| Pricing attribute | gpt-realtime | Qwen3.5-9B |
|---|---|---|
| Input price | $4/1M tokens | $0.1/1M tokens |
| Output price | $16/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | gpt-realtime | Qwen3.5-9B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | 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, function calling: Qwen3.5-9B, tool use: Qwen3.5-9B, and structured outputs: Qwen3.5-9B. Both models share multimodal input, 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, gpt-realtime lists $4/1M input and $16/1M 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 Qwen3.5-9B lower by about $7.48 per million blended tokens. Availability is 1 providers versus 3, so concentration risk also matters.
Choose gpt-realtime 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, gpt-realtime or Qwen3.5-9B?
Qwen3.5-9B supports 262K tokens, while gpt-realtime supports 32K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, gpt-realtime or Qwen3.5-9B?
Qwen3.5-9B is cheaper on tracked token pricing. gpt-realtime costs $4/1M input and $16/1M 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 gpt-realtime or Qwen3.5-9B open source?
gpt-realtime is listed under Proprietary. 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, gpt-realtime 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, gpt-realtime or Qwen3.5-9B?
Both gpt-realtime and Qwen3.5-9B expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run gpt-realtime and Qwen3.5-9B?
gpt-realtime is available on OpenAI API. 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.