GPT-5.5 leads the Intelligence Index at 60.2. Qwen 3.7 Max sits at 56.6, a 3.6-point gap that puts it in 5th place overall but 1st among Chinese models. The gap is real, but so is the price difference: Qwen costs $2.50/$7.50 per million tokens versus GPT-5.5’s $5/$15. Alibaba is offering frontier-adjacent intelligence at half the price of OpenAI’s flagship.
The question isn’t whether GPT-5.5 is smarter. It is. The question is whether that intelligence gap justifies paying 2x more, and whether Qwen’s advantages in context window and autonomous runtime make it the better choice for certain workloads.
This comparison covers benchmarks, pricing, context, speed, coding ability, agent capabilities, and ecosystem. For the full Qwen 3.7 breakdown, see our Qwen 3.7 complete guide.
Quick Specs
| Spec | Qwen 3.7 Max | GPT-5.5 |
|---|---|---|
| Company | Alibaba Cloud | OpenAI |
| Intelligence Index | 56.6 (#5) | 60.2 (#1) |
| Context window | 1,000,000 tokens | 256,000 tokens |
| Input pricing | $2.50 / 1M tokens | $5.00 / 1M tokens |
| Output pricing | $7.50 / 1M tokens | $15.00 / 1M tokens |
| Weights | Closed | Closed |
| Access | API only | API, ChatGPT, Azure, Codex CLI |
| Max autonomous runtime | 35 hours | Task-based |
| Speed (tokens/sec) | ~85 | ~71 |
Qwen is exactly half the price of GPT-5.5 on both input and output. Not a 10x gap like versus Claude, but still meaningful at scale. And Qwen’s 1M context window is nearly 4x larger than GPT-5.5’s 256K.
Benchmark Comparison
Here’s the full breakdown across major evaluation suites:
| Benchmark | Qwen 3.7 Max | GPT-5.5 | Winner |
|---|---|---|---|
| Intelligence Index | 56.6 | 60.2 | GPT-5.5 |
| Terminal-Bench Hard | 50.8% | 78.2% | GPT-5.5 |
| HLE (Humanity’s Last Exam) | 38.1% | 28.1% | Qwen |
| CritPt | 13.4% | 12.8% | Qwen |
| Apex Math | 44.5 | 43.2 | Qwen |
| MCP-Atlas | 76.4% | 75.3% | Qwen |
| SWE-bench Pro (a harder variant than SWE-bench Verified used in other benchmarks) | 52.8% | 58.6% | GPT-5.5 |
| ARC-AGI-2 | 68.3% | 84.6% | GPT-5.5 |
Wins by model: GPT-5.5 takes 6 benchmarks, Qwen takes 2 (CritPt, Apex Math).
The numbers don’t lie. GPT-5.5 is the stronger model across most evaluations. The Terminal-Bench Hard gap is particularly large (78.2% vs 50.8%), showing GPT-5.5’s dominance on complex command-line tasks. ARC-AGI-2 (84.6% vs 68.3%) demonstrates a significant reasoning advantage.
Where Qwen holds its own is on mathematical reasoning. CritPt (13.4% vs 12.8%) and Apex Math (44.5 vs 43.2) show Qwen matching or slightly beating GPT-5.5 on pure mathematical problem-solving. These are narrow wins, but they suggest Qwen’s training prioritized mathematical capability.
Pricing: The 2x Advantage
The cost comparison is straightforward:
| Scenario | Qwen 3.7 Max | GPT-5.5 | Savings with Qwen |
|---|---|---|---|
| Single session (50K in / 5K out) | $0.16 | $0.33 | 52% |
| 100 sessions/month | $16.25 | $32.50 | 50% |
| Heavy agent workload (500K in / 50K out) | $1.63 | $3.25 | 50% |
| 1000 API calls/day (10K in / 2K out) | $40/day | $80/day | 50% |
A consistent 50% savings across all usage patterns. Over a month of heavy usage (1000 calls/day), that’s $1,200 saved. Not life-changing for a well-funded team, but significant for startups, indie developers, and high-volume production systems.
The real question: is GPT-5.5 twice as good? On most benchmarks, the answer is no. GPT-5.5 is roughly 6-15% better depending on the task, not 100% better. You’re paying a premium for incremental quality gains. For a broader pricing perspective, see our AI API pricing comparison.
Context Window: 1M vs 256K
Qwen 3.7 Max’s 1,000,000 token context window is nearly 4x larger than GPT-5.5’s 256,000 tokens. This is one area where Qwen has an unambiguous advantage.
What does 1M tokens get you in practice?
- An entire medium-sized codebase (50-100 files) loaded in context simultaneously
- Full legal contracts, research papers, or book-length documents without chunking
- Extended agent sessions that accumulate history over hours of operation
- RAG pipelines with more retrieved context per query
GPT-5.5’s 256K is still generous and handles most workloads fine. But if you regularly hit context limits, or if you’re building systems that benefit from having more information available at inference time, Qwen’s 4x advantage matters.
One caveat: raw context size doesn’t guarantee effective utilization. GPT-5.5 may retrieve information more accurately from its 256K window than Qwen does from its 1M window. Long-context retrieval benchmarks (like MRCR) would be the definitive test here, but comprehensive head-to-head data on this specific comparison isn’t yet available.
Speed
Qwen 3.7 Max outputs at approximately 85 tokens per second versus GPT-5.5’s 71 tokens per second. A modest speed advantage for Qwen, roughly 20% faster.
For interactive use, both are fast enough that the difference is barely perceptible. Where it matters is in agentic workloads with many sequential inference calls. Over 50 tool-call iterations, Qwen saves a few seconds of cumulative latency. Not a deciding factor, but a nice bonus on top of the cost savings.
Coding Ability
GPT-5.5 leads on SWE-bench Pro (58.6% vs 52.8%), which tests real-world multi-file code changes. That 5.8-point gap translates to GPT-5.5 solving roughly 1 in 10 more coding tasks correctly on the first attempt.
Terminal-Bench Hard shows an even larger gap (78.2% vs 50.8%). This benchmark tests complex command-line task completion, including multi-step operations, error recovery, and system administration tasks. GPT-5.5 is substantially better at terminal-based workflows.
In practice, this means:
- GPT-5.5 produces more reliable code for complex refactoring tasks
- GPT-5.5 handles multi-step terminal operations more successfully
- Qwen is adequate for straightforward code generation but struggles more on hard problems
For developers who rely heavily on AI for complex coding tasks, GPT-5.5 justifies its premium. For simpler code generation, boilerplate, and utility scripts, Qwen performs well enough that the 50% cost savings wins out.
For how Qwen compares to Claude on coding specifically, see our Qwen 3.7 vs Claude Opus 4.7 comparison.
Agent Capabilities
Qwen 3.7 Max supports 35-hour autonomous operation, meaning agents can run continuously for over a day without timing out. It also supports cross-harness execution and Anthropic API compatibility, making it easy to integrate into existing agent frameworks built for Claude or other providers.
GPT-5.5 powers OpenAI’s Codex CLI and integrates with the broader OpenAI ecosystem (Assistants API, function calling, structured outputs). It scores higher on MCP-Atlas (75.3% vs 76.4%), indicating better tool-use orchestration.
The tradeoff is clear: GPT-5.5 is better at individual tool-use steps, but Qwen can sustain agent operation for much longer periods at lower cost. For short-lived agents that need to be maximally capable on each step, GPT-5.5 wins. For long-running agents where cost and runtime matter, Qwen has the edge.
Availability and Ecosystem
GPT-5.5 has the largest ecosystem of any AI model:
- ChatGPT (Free, Plus, Pro, Team, Enterprise)
- OpenAI API
- Azure OpenAI Service
- Codex CLI
- Thousands of third-party integrations
- Extensive SDK support (Python, Node, Go, etc.)
Qwen 3.7 Max has more limited availability:
- Alibaba Cloud Model Studio API
- DashScope API
- Anthropic API compatible endpoints
- Growing but smaller third-party support
GPT-5.5’s ecosystem advantage is significant. If you need first-party apps, enterprise support, compliance certifications, or integration with Azure infrastructure, OpenAI is the safer choice. Qwen’s Anthropic API compatibility helps bridge the gap for custom integrations, but it can’t match the breadth of OpenAI’s platform.
For teams already using OpenAI’s stack, switching to Qwen means giving up Codex CLI, ChatGPT’s interface, and Azure’s enterprise features. For teams building custom API integrations, the switch is more straightforward.
Who Should Choose What
Choose Qwen 3.7 Max if:
- You want frontier-level intelligence at 50% of GPT-5.5’s cost
- You need a 1M token context window (4x larger than GPT-5.5)
- You’re building long-running autonomous agents (35-hour runtime)
- Mathematical reasoning is a priority workload
- You’re comfortable with API-only access and a smaller ecosystem
- You’re building custom integrations rather than relying on first-party apps
Choose GPT-5.5 if:
- You need the highest available intelligence (60.2 Intelligence Index)
- Complex coding and terminal tasks are your primary use case
- You want the broadest ecosystem and enterprise support
- You need Azure integration or compliance certifications
- Tool-use accuracy matters more than cost
- You rely on ChatGPT or Codex CLI as part of your workflow
Verdict
GPT-5.5 is the better model. The 3.6-point Intelligence Index gap, the massive Terminal-Bench Hard lead, and the stronger coding benchmarks all point to GPT-5.5 being meaningfully more capable. Alibaba hasn’t closed the gap yet.
But “better” and “worth 2x the price” are different questions. For workloads where Qwen 3.7 Max is good enough (and at 56.6 on the Intelligence Index, it’s good enough for a lot), the 50% cost savings and 4x context window make it a rational choice. The 35-hour autonomous runtime is also unique and valuable for specific agent architectures.
The honest recommendation: use GPT-5.5 for your hardest tasks (complex refactoring, multi-step terminal operations, novel problem-solving) and route simpler workloads to Qwen 3.7 Max. A routing layer that sends easy tasks to Qwen and hard tasks to GPT-5.5 gives you the best of both worlds. For how this compares to the DeepSeek alternative, see DeepSeek V3 vs GPT-5.
FAQ
Is Qwen 3.7 Max better than GPT-5.5?
No, not overall. GPT-5.5 leads on the Intelligence Index (60.2 vs 56.6), Terminal-Bench Hard (78.2% vs 50.8%), SWE-bench Pro (58.6% vs 52.8%), and ARC-AGI-2 (84.6% vs 68.3%). Qwen only wins on CritPt (13.4% vs 12.8%) and Apex Math (44.5 vs 43.2). GPT-5.5 is the stronger model across most tasks.
How much cheaper is Qwen 3.7 Max than GPT-5.5?
Exactly 50% cheaper. Qwen costs $2.50/$7.50 per million tokens (input/output) versus GPT-5.5’s $5/$15. A typical coding session costs $0.16 with Qwen versus $0.33 with GPT-5.5.
Which model has a larger context window?
Qwen 3.7 Max has a 1,000,000 token context window, nearly 4x larger than GPT-5.5’s 256,000 tokens. For large codebases, long documents, or extended agent sessions, Qwen’s context advantage is significant.
Can Qwen 3.7 Max replace GPT-5.5 for coding tasks?
For simple code generation, yes. For complex multi-file refactoring and terminal operations, GPT-5.5 is meaningfully better (58.6% vs 52.8% on SWE-bench Pro, 78.2% vs 50.8% on Terminal-Bench Hard). If coding quality is your top priority, GPT-5.5 justifies the premium.
Is Qwen 3.7 Max open source?
No. Qwen 3.7 Max is closed-weight and API-only. Earlier Qwen models (3.5, 3.6) were released with open weights, but the 3.7 Max flagship is proprietary. You cannot download or self-host it.
Which model is better for AI agents?
It depends on the agent architecture. GPT-5.5 scores higher on tool-use benchmarks (MCP-Atlas 75.3% vs 76.4%) and has better per-step accuracy. Qwen 3.7 Max supports 35-hour autonomous operation and costs 50% less, making it better for long-running, cost-sensitive agent workloads.
How does Qwen 3.7 Max compare to other Chinese AI models?
Qwen 3.7 Max is the #1 ranked Chinese model on the Intelligence Index at 56.6, ahead of DeepSeek V4 and other domestic competitors. It’s Alibaba’s first closed-weight frontier model, signaling a strategic shift from their previous open-source approach.
Can I use Qwen 3.7 Max with existing OpenAI client libraries?
Not directly, but Qwen 3.7 Max supports Anthropic API compatibility. You’d need to use Anthropic-compatible client libraries or adapt your OpenAI calls. Some third-party tools like OpenRouter provide unified access to both models through a single API.