Gemma 4 12B IT vs Qianfan-OCR-Fast
Gemma 4 12B IT (2026) and Qianfan-OCR-Fast (2026) are frontier reasoning models from Google DeepMind and Baidu AI. Gemma 4 12B IT ships a 256k-token context window, while Qianfan-OCR-Fast ships a 66k-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.
Gemma 4 12B IT is safer overall; choose Qianfan-OCR-Fast when vision-heavy evaluation matters.
Decision scorecard
Local evidence first| Signal | Gemma 4 12B IT | Qianfan-OCR-Fast |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | multimodal apps |
| Decision fit | Coding, RAG, and Agents | Vision |
| Context window | 256k | 66k |
| Cheapest output | - | $2.81/1M tokens |
| Provider routes | 2 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 4 12B IT has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Gemma 4 12B IT has broader tracked provider coverage for fallback and procurement flexibility.
- Gemma 4 12B IT uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags Gemma 4 12B IT for Coding, RAG, and Agents.
- Local decision data tags Qianfan-OCR-Fast for Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Gemma 4 12B IT
Unavailable
No complete token price in local provider data
Qianfan-OCR-Fast
$1,247
Cheapest tracked route/tier: OpenRouter
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Gemma 4 12B IT and Qianfan-OCR-Fast; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- No overlapping tracked provider route is sourced for Qianfan-OCR-Fast and Gemma 4 12B IT; plan for SDK, billing, or endpoint changes.
- Gemma 4 12B IT adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-06-03 | 2026-04-20 |
| Context window | 256k | 66k |
| Parameters | 11.9B | — |
| Architecture | encoder free unified multimodal | decoder only |
| License | Apache 2.0 | Proprietary |
| Knowledge cutoff | 2025-01 | - |
Pricing and availability
| Pricing attribute | Gemma 4 12B IT | Qianfan-OCR-Fast |
|---|---|---|
| Input price | - | $0.68/1M tokens |
| Output price | - | $2.81/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 4 12B IT | Qianfan-OCR-Fast |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | 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 reasoning mode: Gemma 4 12B IT, function calling: Gemma 4 12B IT, tool use: Gemma 4 12B IT, and structured outputs: Gemma 4 12B IT. Both models share vision and 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.
Pricing coverage is uneven: Gemma 4 12B IT has no token price sourced yet and Qianfan-OCR-Fast has $0.68/1M input tokens. Provider availability is 2 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemma 4 12B IT when reasoning depth, larger context windows, and broader provider choice are central to the workload. Choose Qianfan-OCR-Fast when vision-heavy evaluation 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, Gemma 4 12B IT or Qianfan-OCR-Fast?
Gemma 4 12B IT supports 256k 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.
Is Gemma 4 12B IT or Qianfan-OCR-Fast open source?
Gemma 4 12B IT is listed under Apache 2.0. Qianfan-OCR-Fast is listed under Proprietary. 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, Gemma 4 12B IT or Qianfan-OCR-Fast?
Both Gemma 4 12B IT and Qianfan-OCR-Fast 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, Gemma 4 12B IT or Qianfan-OCR-Fast?
Both Gemma 4 12B IT and Qianfan-OCR-Fast expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for reasoning mode, Gemma 4 12B IT or Qianfan-OCR-Fast?
Gemma 4 12B IT has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Gemma 4 12B IT and Qianfan-OCR-Fast?
Gemma 4 12B IT is available on Hugging Face Inference Endpoints and Kaggle Models. Qianfan-OCR-Fast is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-03. Data sourced from public model cards and provider documentation.