Model Comparison
3.5 (1210)
vs. Qwen2.5 Max
Comparing 2 AI models · 7 benchmarks · OpenChat, Alibaba
Recommended Pick
AlQwen2.5 Max7 metric winsStrongest on: Reasoning, Intelligence, GPQA
Lowest Price
3.5 (1210)
$0.00/1M input price
Best Reasoning
Qwen2.5 Max
36.0 reasoning score
Blends available reasoning benchmarks
Composite Indices
Higher is better; speed and price are normalized
Standard Benchmarks
Only benchmarks with data are shown
Differences That Matter
Reasoning gap
Qwen2.5 Max leads 3.5 (1210) by 23.7 points on reasoning.
Top-pick rationale
Qwen2.5 Max wins 7 measurable categories, including Reasoning, Intelligence, GPQA, MMLU Pro.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
3.5 (1210)
OpenChat
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Qwen2.5 Max
Alibaba
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
Chat with leading AI models
Use Claude, ChatGPT, Gemini alongside with EU-Hosted Models like Deepseek, Qwen & Kimi.
EU-hosted inference
Servers in Germany & Finland. Designed to meet strict GDPR and ISO 27001 compliance requirements.
Full Comparison
| Metric | Op 3.5 (1210) | Top Pick Al Qwen2.5 Max |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.00/1M | $0.00/1M |
| Output Cost | $0.00/1M | $0.00/1M |
| Specifications | ||
| Organization | OpenChat | Alibaba |
| Release Date | Dec 18, 2023 | Jan 28, 2025 |
| Performance & Speed | ||
| Throughput | — | — |
| TTFT | — | — |
| Latency | — | — |
| Composite Indices | ||
| Reasoning Score | 12.3 | 36.0 |
| Intelligence | 3.0 | 10.2 |
| Standard Benchmarks | ||
| GPQA | 23.0% | 58.7% |
| MMLU Pro | 31.0% | 76.2% |
| HLE | 4.8% | 4.5% |
| LiveCodeBench | 11.5% | 35.9% |
| MATH 500 | 30.7% | 83.5% |
| AIME (Original) | 0.0% | 23.3% |
| SciCode | — | 33.7% |
Key Takeaways
3.5 (1210) offers the best value at $0.00/1M,making it ideal for high-volume applications and cost-conscious projects.
Qwen2.5 Max has the strongest reasoning profile with a 36.0 reasoning score,combining the available reasoning-heavy benchmarks.
All models support context windows of ∞+ tokens,suitable for processing lengthy documents and maintaining extended conversations.
When to Choose Each Model
3.5 (1210)
- Cost-sensitive applications
- High-volume processing
Qwen2.5 Max
- Complex reasoning tasks
- Research & analysis
