LLM Reference

Gemma 4 12B vs Qianfan-OCR-Fast

Gemma 4 12B (2026) and Qianfan-OCR-Fast (2026) are frontier reasoning models from Google DeepMind and Baidu AI. Gemma 4 12B 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 is safer overall; choose Qianfan-OCR-Fast when vision-heavy evaluation matters.

Decision scorecard

Local evidence first
SignalGemma 4 12BQianfan-OCR-Fast
Best forreasoning-heavy apps, multimodal apps, and tool-calling agentsmultimodal apps
Decision fitRAG, Agents, and Long contextVision
Context window256k66k
Cheapest output-$2.81/1M tokens
Provider routes2 tracked1 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Gemma 4 12B when...
  • Gemma 4 12B has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Gemma 4 12B has broader tracked provider coverage for fallback and procurement flexibility.
  • Gemma 4 12B uniquely exposes Reasoning, Function calling, and Tool use in local model data.
  • Local decision data tags Gemma 4 12B for RAG, Agents, and Long context.
Choose Qianfan-OCR-Fast when...
  • 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

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

Gemma 4 12B -> Qianfan-OCR-Fast
  • No overlapping tracked provider route is sourced for Gemma 4 12B 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.
Qianfan-OCR-Fast -> Gemma 4 12B
  • No overlapping tracked provider route is sourced for Qianfan-OCR-Fast and Gemma 4 12B; plan for SDK, billing, or endpoint changes.
  • Gemma 4 12B adds Reasoning, Function calling, and Tool use in local capability data.

Specs

Specification
Released2026-06-032026-04-20
Context window256k66k
Parameters11.9B
Architectureencoder free unified multimodaldecoder only
LicenseApache 2.0Proprietary
Knowledge cutoff2025-01-

Pricing and availability

Pricing attributeGemma 4 12BQianfan-OCR-Fast
Input price-$0.68/1M tokens
Output price-$2.81/1M tokens
Providers

Capabilities

CapabilityGemma 4 12BQianfan-OCR-Fast
VisionYesYes
MultimodalYesYes
ReasoningYesNo
Function callingYesNo
Tool useYesNo
Structured outputsNoNo
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on reasoning mode: Gemma 4 12B, function calling: Gemma 4 12B, and tool use: Gemma 4 12B. 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 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 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. 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, Gemma 4 12B or Qianfan-OCR-Fast?

Gemma 4 12B 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 or Qianfan-OCR-Fast open source?

Gemma 4 12B 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 or Qianfan-OCR-Fast?

Both Gemma 4 12B 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 or Qianfan-OCR-Fast?

Both Gemma 4 12B 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for reasoning mode, Gemma 4 12B or Qianfan-OCR-Fast?

Gemma 4 12B 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 and Qianfan-OCR-Fast?

Gemma 4 12B 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.