Qianfan-OCR-Fast vs ShieldGemma 9B
Qianfan-OCR-Fast (2026) and ShieldGemma 9B (2024) are compact production models from Baidu AI and Google DeepMind. Qianfan-OCR-Fast ships a 66k-token context window, while ShieldGemma 9B ships a 8k-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.
Qianfan-OCR-Fast fits 8x more tokens; pick it for long-context work and ShieldGemma 9B for tighter calls.
Decision scorecard
Local evidence first| Signal | Qianfan-OCR-Fast | ShieldGemma 9B |
|---|---|---|
| Best for | multimodal apps | general production evaluation |
| Decision fit | Vision | Classification |
| Context window | 66k | 8k |
| Cheapest output | $2.81/1M tokens | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Qianfan-OCR-Fast has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qianfan-OCR-Fast uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Qianfan-OCR-Fast for Vision.
- Local decision data tags ShieldGemma 9B for Classification.
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
ShieldGemma 9B
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 ShieldGemma 9B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- No overlapping tracked provider route is sourced for ShieldGemma 9B and Qianfan-OCR-Fast; plan for SDK, billing, or endpoint changes.
- Qianfan-OCR-Fast adds Vision and Multimodal in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-20 | 2024-07-01 |
| Context window | 66k | 8k |
| Parameters | — | 9B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Gemma |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Qianfan-OCR-Fast | ShieldGemma 9B |
|---|---|---|
| Input price | $0.68/1M tokens | - |
| Output price | $2.81/1M tokens | - |
| Providers |
Capabilities
| Capability | Qianfan-OCR-Fast | ShieldGemma 9B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| 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 differs most on vision: Qianfan-OCR-Fast and multimodal input: Qianfan-OCR-Fast. 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: Qianfan-OCR-Fast has $0.68/1M 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 Qianfan-OCR-Fast 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. 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 ShieldGemma 9B?
Qianfan-OCR-Fast supports 66k 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Is Qianfan-OCR-Fast or ShieldGemma 9B open source?
Qianfan-OCR-Fast is listed under Proprietary. ShieldGemma 9B is listed under Gemma. 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 ShieldGemma 9B?
Qianfan-OCR-Fast 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, Qianfan-OCR-Fast or ShieldGemma 9B?
Qianfan-OCR-Fast 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 Qianfan-OCR-Fast and ShieldGemma 9B?
Qianfan-OCR-Fast is available on OpenRouter. 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.
When should I pick Qianfan-OCR-Fast over ShieldGemma 9B?
Qianfan-OCR-Fast fits 8x more tokens; pick it for long-context work and ShieldGemma 9B for tighter calls. If your workload also depends on long-context analysis, start with Qianfan-OCR-Fast; if it depends on provider fit, run the same evaluation with ShieldGemma 9B.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.