Qianfan-OCR-Fast vs Qwen3.5-4B
Qianfan-OCR-Fast (2026) and Qwen3.5-4B (2026) are compact production models from Baidu AI and Alibaba. Qianfan-OCR-Fast ships a 66k-token context window, while Qwen3.5-4B ships a 262k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3.5-4B fits 4x more tokens; pick it for long-context work and Qianfan-OCR-Fast for tighter calls.
Decision scorecard
Local evidence first| Signal | Qianfan-OCR-Fast | Qwen3.5-4B |
|---|---|---|
| Best for | multimodal apps | multimodal apps |
| Decision fit | Vision | Long context and Vision |
| Context window | 66k | 262k |
| Cheapest output | $2.81/1M tokens | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Qianfan-OCR-Fast has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Qianfan-OCR-Fast for Vision.
- Qwen3.5-4B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Qwen3.5-4B for Long context and Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Qianfan-OCR-Fast
$1,247
Cheapest tracked route/tier: OpenRouter
Qwen3.5-4B
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Qianfan-OCR-Fast and Qwen3.5-4B; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Qwen3.5-4B and Qianfan-OCR-Fast; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-20 | 2026-03-02 |
| Context window | 66k | 262k |
| Parameters | — | 4B |
| Architecture | decoder only | - |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Qianfan-OCR-Fast | Qwen3.5-4B |
|---|---|---|
| Input price | $0.68/1M tokens | - |
| Output price | $2.81/1M tokens | - |
| Providers | - |
Capabilities
| Capability | Qianfan-OCR-Fast | Qwen3.5-4B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| 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 is close: both models cover vision and multimodal input. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
Pricing coverage is uneven: Qianfan-OCR-Fast has $0.68/1M input tokens and Qwen3.5-4B has no token price sourced yet. Provider availability is 1 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Qianfan-OCR-Fast when vision-heavy evaluation and broader provider choice are central to the workload. Choose Qwen3.5-4B 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. 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, Qianfan-OCR-Fast or Qwen3.5-4B?
Qwen3.5-4B supports 262k tokens, while Qianfan-OCR-Fast supports 66k 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.
Is Qianfan-OCR-Fast or Qwen3.5-4B open source?
Qianfan-OCR-Fast is listed under Proprietary. Qwen3.5-4B 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, Qianfan-OCR-Fast or Qwen3.5-4B?
Both Qianfan-OCR-Fast and Qwen3.5-4B expose vision. 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.
Which is better for multimodal input, Qianfan-OCR-Fast or Qwen3.5-4B?
Both Qianfan-OCR-Fast and Qwen3.5-4B 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 Qianfan-OCR-Fast and Qwen3.5-4B?
Qianfan-OCR-Fast is available on OpenRouter. Qwen3.5-4B is available on the tracked providers still being sourced. 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.
When should I pick Qianfan-OCR-Fast over Qwen3.5-4B?
Qwen3.5-4B fits 4x more tokens; pick it for long-context work and Qianfan-OCR-Fast for tighter calls. If your workload also depends on vision-heavy evaluation, start with Qianfan-OCR-Fast; if it depends on long-context analysis, run the same evaluation with Qwen3.5-4B.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.