Gemma 2 2B vs Qianfan-OCR-Fast
Gemma 2 2B (2024) and Qianfan-OCR-Fast (2026) are compact production models from Google DeepMind and Baidu AI. Gemma 2 2B ships a 8k-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.
Qianfan-OCR-Fast fits 8x more tokens; pick it for long-context work and Gemma 2 2B for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 2 2B | Qianfan-OCR-Fast |
|---|---|---|
| Best for | general production evaluation | multimodal apps |
| Decision fit | General | Vision |
| Context window | 8k | 66k |
| Cheapest output | - | $2.81/1M tokens |
| Provider routes | 0 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use Gemma 2 2B when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- Qianfan-OCR-Fast has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qianfan-OCR-Fast has broader tracked provider coverage for fallback and procurement flexibility.
- Qianfan-OCR-Fast uniquely exposes Vision and Multimodal in local model data.
- 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 2 2B
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 2 2B and Qianfan-OCR-Fast; plan for SDK, billing, or endpoint changes.
- Qianfan-OCR-Fast adds Vision and Multimodal in local capability data.
- No overlapping tracked provider route is sourced for Qianfan-OCR-Fast and Gemma 2 2B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-31 | 2026-04-20 |
| Context window | 8k | 66k |
| Parameters | 2B | — |
| Architecture | decoder only | decoder only |
| License | Gemma | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 2 2B | Qianfan-OCR-Fast |
|---|---|---|
| Input price | - | $0.68/1M tokens |
| Output price | - | $2.81/1M tokens |
| Providers | - |
Capabilities
| Capability | Gemma 2 2B | Qianfan-OCR-Fast |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | 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 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: Gemma 2 2B has no token price sourced yet and Qianfan-OCR-Fast has $0.68/1M input tokens. Provider availability is 0 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 2 2B when provider fit are central to the workload. Choose Qianfan-OCR-Fast when long-context analysis, larger context windows, and broader provider choice 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 2 2B or Qianfan-OCR-Fast?
Qianfan-OCR-Fast supports 66k tokens, while Gemma 2 2B supports 8k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 2 2B or Qianfan-OCR-Fast open source?
Gemma 2 2B is listed under Gemma. 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 2 2B or Qianfan-OCR-Fast?
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, Gemma 2 2B or Qianfan-OCR-Fast?
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 Gemma 2 2B and Qianfan-OCR-Fast?
Gemma 2 2B is available on the tracked providers still being sourced. Qianfan-OCR-Fast is available on OpenRouter. 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 Gemma 2 2B over Qianfan-OCR-Fast?
Qianfan-OCR-Fast fits 8x more tokens; pick it for long-context work and Gemma 2 2B for tighter calls. If your workload also depends on provider fit, start with Gemma 2 2B; if it depends on long-context analysis, run the same evaluation with Qianfan-OCR-Fast.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.