Gemini 2.5 Pro Experimental vs Qwen-Long
Compare pricing, context windows, and strengths for Gemini 2.5 Pro Experimental by Google and Qwen-Long by Alibaba Cloud - and see how to put either to work in Appaca.
Gemini 2.5 Pro Experimental
Google's most advanced thinking model, leading benchmarks in reasoning, science, math, and coding with a massive multimodal context window.
View Gemini 2.5 Pro ExperimentalQwen-Long
Long-context model with 10M tokens for huge document analysis and summarization.
View Qwen-LongGemini 2.5 Pro Experimental vs Qwen-Long at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | Gemini 2.5 Pro Experimental | Qwen-Long |
|---|---|---|
| Provider | Alibaba Cloud | |
| Model type | Text | Text |
| Context window | 1.05M tokens | 10M tokens |
| Input price | $1.5 / 1M tokens | $0.072 / 1M tokens |
| Output price | $6 / 1M tokens | $0.287 / 1M tokens |
| Status | Current | Current |
How Gemini 2.5 Pro Experimental and Qwen-Long differ
What the numbers mean in practice when choosing between Gemini 2.5 Pro Experimental and Qwen-Long.
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Qwen-Long is 95% cheaper on input tokens ($0.072 vs $1.5 per million), which adds up quickly in document-heavy workloads.
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Qwen-Long is 95% cheaper on output tokens ($0.287 vs $6 per million) - the bigger factor for tools that generate long documents.
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Qwen-Long's 10M tokens context window is roughly 9.5x larger than Gemini 2.5 Pro Experimental's 1.05M tokens, so it can work across bigger codebases, contracts, or archives in one pass.
Strengths side by side
Where each model shines, according to benchmarks and provider positioning.
Gemini 2.5 Pro Experimental
1. State-of-the-art reasoning performance
- #1 on LMArena human preference leaderboard.
- Excels at advanced reasoning benchmarks like GPQA and AIME 2025.
- Achieves 18.8% on Humanity's Last Exam (no tools), representing frontier human-level reasoning.
2. New “thinking model” architecture
- Built with explicit reasoning steps internally before responding.
- Handles complex, multi-stage logic with higher accuracy and fewer hallucinations.
3. Elite science and mathematics capabilities
- Leads in math and science tasks across industry benchmarks.
- High performance without costly inference tricks like majority voting.
4. Exceptional coding abilities
- Major leap over Gemini 2.0 in coding performance.
- 63.8% on SWE-Bench Verified with custom agent setup.
- Strong at code transformation, debugging, and building agentic apps.
- Capable of generating full applications (e.g., a playable video game) from a single-line prompt.
5. Massive multimodal context
- Ships with a 1,000,000 token window (2M coming soon).
- Handles entire documents, datasets, video sequences, audio files, and large codebases.
- Maintains strong performance even at extreme context lengths.
6. Native multimodality across all inputs
- Understands and reasons over text, images, audio, video, and code.
- Designed for real-world, multi-source problem-solving and agent workflows.
7. Consistent high-quality outputs
- Improved post-training results in more accurate, coherent, and stylistically strong responses.
- Higher reliability across complex workloads.
8. Early availability for developers
- Available today in Google AI Studio for experimentation.
- Coming soon to Vertex AI with higher rate limits and production-ready access.
Qwen-Long
1. Extremely long context window
- Up to 10 million tokens.
2. Ideal for document-heavy workflows
- Legal, financial, RAG, compliance, research.
3. Low-cost for large-scale ingestion
- Optimized pricing for big inputs.
Use Gemini 2.5 Pro Experimental or Qwen-Long - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by Gemini 2.5 Pro Experimental or Qwen-Long - connected to your real data and ready for your whole team. No code, no deployment.
Describe it, and it's built
Tell the Appaca agent the internal tool you need and it builds a working app powered by Gemini 2.5 Pro Experimental or Qwen-Long. No code, no API keys, no deployment.
Switch models without rebuilding
Start on Gemini 2.5 Pro Experimental, test the same tool on Qwen-Long, and keep whichever performs better - the rest of your app stays exactly as it is.
Automated for the whole team
Schedule tools to run on autopilot - daily digests, weekly reports, real-time triggers - and share them with your whole team from one workspace.
Describe it, and it's built
Tell the Appaca agent what your team needs and it builds a working app powered by Gemini 2.5 Pro Experimental or Qwen-Long - connected to the tools you already use.
Related comparisons
See how Gemini 2.5 Pro Experimental and Qwen-Long stack up against other models in the directory.
FAQs
Qwen-Long is generally cheaper: $0.072 input / $0.287 output per million tokens, versus $1.5 / $6 for Gemini 2.5 Pro Experimental. Actual cost depends on how many tokens your workload reads and writes.
Qwen-Long has the larger context window at 10M tokens, compared to 1.05M tokens for Gemini 2.5 Pro Experimental. A larger window means the model can consider more text at once - useful for long contracts, codebases, or months of records.
It depends on the job. Compare the pricing, context window, and strengths above against your workload - and remember the choice isn't permanent. In Appaca you can build a tool on Gemini 2.5 Pro Experimental, test the same tool on Qwen-Long, and switch at any time without rebuilding anything.
Yes. Appaca is a no-code AI workspace: describe the internal tool your team needs and the Appaca agent builds it as a working app powered by Gemini 2.5 Pro Experimental, Qwen-Long, or any other model in the directory - with a built-in database, team access, and integrations. No API keys to wire up and nothing to deploy.
Build AI tools with Gemini 2.5 Pro Experimental or Qwen-Long
Describe the tool your team needs and get a working app powered by the model you choose - with a built-in database, team access, and integrations. No code, no deployment.