| Signal | GPT-5.1-Codex-Max | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 86 | +14 | |
Context window size | 89 | -6 | |
Output Capacity | 85 | +5 | |
Pricing Tier | 10 | +10 | |
Recency | 100 | -- | |
Versatility | 50 | -17 | |
| Overall Result | 3 wins | of 6 | 2 wins |
24
days ranked higher
4
days
2
days ranked higher
OpenAI
Alibaba
Qwen3.5-Flash saves you $595.00/month
That's $7140.00/year compared to GPT-5.1-Codex-Max at your current usage level of 100K calls/month.
| Metric | GPT-5.1-Codex-Max | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 66 | 62 | GPT-5.1-Codex-Max |
| Rank | #24 | #45 | GPT-5.1-Codex-Max |
| Quality Rank | #24 | #45 | GPT-5.1-Codex-Max |
| Adoption Rank | #24 | #45 | GPT-5.1-Codex-Max |
| Parameters | -- | -- | -- |
| Context Window | 400K | 1000K | Qwen3.5-Flash |
| Pricing | $1.25/$10.00/M | $0.10/$0.40/M | -- |
| Signal Scores | |||
| Capabilities | 86 | 71 | GPT-5.1-Codex-Max |
| Context window size | 89 | 95 | Qwen3.5-Flash |
| Output Capacity | 85 | 80 | GPT-5.1-Codex-Max |
| Pricing Tier | 10 | 0 | GPT-5.1-Codex-Max |
| Recency | 100 | 100 | GPT-5.1-Codex-Max |
| Versatility | 50 | 67 | Qwen3.5-Flash |
GPT-5.1-Codex-Max has a moderate advantage with a 3.8999999999999986-point lead in composite score. It wins on more signal dimensions, but Qwen3.5-Flash has specific strengths that could make it the better choice for certain workflows.
Best for Quality
GPT-5.1-Codex-Max
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-Flash
96% lower pricing; better value at scale
Best for Reliability
GPT-5.1-Codex-Max
Higher uptime and faster response speeds
Best for Prototyping
GPT-5.1-Codex-Max
Stronger community support and better developer experience
Best for Production
GPT-5.1-Codex-Max
Wider enterprise adoption and proven at scale
by OpenAI
GPT-5.1-Codex-Max currently scores higher (66 vs 62), but the best choice depends on your specific use case, budget, and requirements.
GPT-5.1-Codex-Max is ranked #24 and Qwen3.5-Flash is ranked #45. Rankings are based on a composite score from multiple signals including benchmarks, community sentiment, and adoption metrics.
Compare the detailed pricing breakdown above to see which model offers better value for your usage pattern.