Three flagship models shipped within five weeks: Alibaba’s Qwen3.7-Max-Preview, OpenAI’s GPT-5.5, and Anthropic’s Claude Opus 4.7. All three are near the top of current AI benchmarks, but they are not interchangeable. Qwen3.7-Max ranked #1 on the Artificial Analysis leaderboard, GPT-5.5 posts the highest raw intelligence score, and Claude Opus 4.7 leads on human-preference quality.
This guide compares the three models across reasoning, coding, context window, pricing, availability, and latency. Use it as a practical decision framework: pick the model that fits your workload, then validate it with your own prompts, token mix, and latency constraints.
TL;DR
Use this shortcut:
- GPT-5.5: best fit for coding agents, terminal automation, and token-efficient agent loops.
- Claude Opus 4.7: best fit for large-codebase engineering, user-facing assistants, and human-preferred output.
- Qwen3.7-Max-Preview: best fit for evaluation, long-context pilots, and cost-sensitive planning, but not production yet because it is preview-only.
Benchmark summary:
- GPT-5.5 has the highest Artificial Analysis Intelligence Index score: 60.
- Qwen3.7-Max-Preview is listed at #1 overall on the Artificial Analysis leaderboard with a score of 57.
- Claude Opus 4.7 also scores 57 and leads the three on LM Arena human-preference Elo.
- GPT-5.5 leads SWE-bench Verified.
- Claude Opus 4.7 leads the harder SWE-bench Pro.
- Qwen3.7-Max has a 1M-token context window, but no public production API or published pricing yet.
The three models at a glance
Before choosing a model, check two things first:
- Is it generally available for production?
- Does it have public pricing and stable API access?
That alone separates Qwen from GPT-5.5 and Claude Opus 4.7.
Qwen3.7-Max-Preview
Qwen3.7-Max is Alibaba’s flagship reasoning model, previewed in mid-May 2026 and announced around the Alibaba Cloud Summit.
It focuses on:
- extended thinking
- agentic coding
- tool use
- long-context reasoning
- 1.0M-token context
The key limitation is availability. As of late May 2026, Qwen3.7-Max is preview-only. It has no public API endpoint and no open weights. Access runs through Alibaba Cloud Model Studio and Qwen Studio.
Alibaba has also said Qwen3.7-Plus will ship as open source while Qwen3.7-Max stays proprietary. If open weights matter to your architecture or compliance model, that distinction matters.
GPT-5.5
GPT-5.5 is OpenAI’s agentic-focused reasoning model, released April 23, 2026.
It is designed for autonomous workflows such as:
- terminal use
- browser tasks
- tool calling
- long-running agent loops
- code repair and automation
OpenAI ships GPT-5.5 in multiple effort tiers. The public Artificial Analysis numbers use the xhigh variant. It supports a 1M-token context window in the API and a smaller 400K-token window inside Codex.
GPT-5.5 is generally available through the OpenAI API today.
Claude Opus 4.7
Claude Opus 4.7 is Anthropic’s flagship model, released April 16, 2026 as an upgrade to Opus 4.6.
It is strongest in:
- advanced software engineering
- large-codebase reasoning
- PR-style coding tasks
- user-facing conversational quality
- long-context analysis
Claude Opus 4.7 uses adaptive reasoning, supports a 1.0M-token context window, and is available through the Anthropic API, Amazon Bedrock, and Google Vertex AI.
Reasoning and intelligence benchmarks
The “Qwen is #1” claim is real, but it needs context.
Artificial Analysis Intelligence Index
The Artificial Analysis Intelligence Index combines ten evaluations across reasoning, knowledge, math, and coding.
As of late May 2026:
| Model | Score | Notes |
|---|---|---|
| Qwen3.7-Max-Preview | 57 | Listed #1 of 218 overall |
| GPT-5.5 xhigh | 60 | Highest raw score of the three |
| Claude Opus 4.7 max | 57 | Listed #3 in its tracked class |
So the practical interpretation is:
- Qwen3.7-Max holds the top overall leaderboard position.
- GPT-5.5 has the higher raw intelligence score.
- Claude Opus 4.7 is effectively in the same top tier.
Do not pick a model from this index alone. Use it to shortlist candidates, then test them on your own prompts.
One production caveat: Artificial Analysis notes that Qwen3.7-Max generated 97M output tokens during evaluation, compared with an average around 26M. That means Qwen may be more verbose, which affects both latency and cost.
LM Arena human-preference Elo
Benchmarks test correctness on fixed tasks. LM Arena tests which answer humans prefer in blind comparisons.
The current LM Arena text leaderboard shows a different ranking:
| Model | Approx. Elo | Rank |
|---|---|---|
| Claude Opus 4.7 | ~1,492 | #4 |
| GPT-5.5 | ~1,478 | #11 |
| Qwen3.7-Max-Preview | ~1,475 | #14, preliminary |
This matters if you are building:
- chatbots
- copilots
- support assistants
- writing tools
- user-facing agents
For user-facing quality, Claude Opus 4.7 has the strongest signal here. Qwen’s score is still preliminary, with fewer votes, so treat it as unstable.
Coding ability
All three models are marketed for coding, but their strengths differ.
SWE-bench Verified
On SWE-bench Verified, which measures real GitHub issue resolution, SWE-bench leaderboard tracking from May 2026 reports:
| Model | SWE-bench Verified |
|---|---|
| GPT-5.5 | 88.7% |
| Claude Opus 4.7 | 87.6% |
| Qwen3.7-Max-Preview | Not published |
GPT-5.5 leads here, but the margin over Claude Opus 4.7 is narrow.
SWE-bench Pro
On the harder SWE-bench Pro benchmark:
| Model | SWE-bench Pro |
|---|---|
| Claude Opus 4.7 | ~64% |
| GPT-5.5 | ~59% |
| Qwen3.7-Max-Preview | Not published |
Claude Opus 4.7 is stronger on broad codebase reasoning and harder pull-request-style tasks.
Practical coding guidance
Use GPT-5.5 when your coding agent needs to:
- run terminal commands
- iterate through shell workflows
- repair issues autonomously
- keep output tokens under control
- perform many sequential tool calls
Use Claude Opus 4.7 when your task requires:
- reasoning across many files
- large architectural changes
- complex refactors
- PR-quality implementation work
- cleaner explanations for developers
Qwen3.7-Max-Preview has strong LM Arena coding-category performance, but no published SWE-bench score yet. Do not assume SWE-bench performance until controlled numbers are available.
For IDE-agent workflows, see the deeper comparison of Cursor Composer 2.5 against Opus 4.7 and GPT-5.5.
Context window
All three models provide roughly million-token context, but implementation details matter.
| Model | Context window |
|---|---|
| Qwen3.7-Max-Preview | 1.0M tokens |
| Claude Opus 4.7 | 1.0M tokens |
| GPT-5.5 | 1M API / ~922K effective / 400K Codex |
A 1M-token window is useful for:
- loading large repositories
- analyzing long contracts
- processing transcripts
- reviewing documentation sets
- maintaining long agent traces
However, a large context window does not guarantee reliable recall at every depth. If long-context performance is critical, run tests like:
1. Place key facts near the beginning, middle, and end of the context.
2. Ask retrieval questions about each location.
3. Ask synthesis questions that require combining facts from multiple sections.
4. Measure accuracy, omissions, latency, and output length.
Also check which product surface you are using. GPT-5.5 has a 1M-token API window, but Codex caps at 400K.
Pricing
Pricing is uneven because Qwen3.7-Max-Preview has no announced public API price.
As of late May 2026:
| Model | Input price / 1M tokens | Output price / 1M tokens |
|---|---|---|
| GPT-5.5 xhigh | $5.00 | $30.00 |
| Claude Opus 4.7 max | $6.25 | $25.00 |
| Qwen3.7-Max-Preview | Not announced | Not announced |
GPT-5.5 is cheaper on input. Claude Opus 4.7 is cheaper on output.
Use this rule of thumb:
Long prompt + short answer -> GPT-5.5 may be cheaper
Short prompt + long answer -> Claude Opus 4.7 may be cheaper
Very high volume workload -> wait for Qwen pricing, then benchmark real output volume
For reference, Qwen3.6-Max-Preview was priced around $1.30 per million input tokens and $7.80 per million output tokens through Alibaba Cloud. If Qwen3.7-Max lands near that range, it could be much cheaper than GPT-5.5 and Claude Opus 4.7.
But do not rely only on per-token pricing. Qwen’s reported verbosity means real cost could rise quickly if it emits far more output tokens.
A practical cost test should log:
{
"model": "model-name",
"input_tokens": 0,
"output_tokens": 0,
"total_tokens": 0,
"latency_ms": 0,
"estimated_cost_usd": 0
}
Then compare cost per successful task, not just cost per token.
For more optimization tactics, see how to reduce agent token costs from the CLI.
Availability and openness
Availability may decide the model before benchmarks do.
| Model | Availability | Open weights |
|---|---|---|
| GPT-5.5 | GA through OpenAI API and Codex | No |
| Claude Opus 4.7 | GA through Anthropic API, Bedrock, Vertex AI | No |
| Qwen3.7-Max-Preview | Preview only through Model Studio / Qwen Studio | No |
For production systems today:
- GPT-5.5 is production-ready.
- Claude Opus 4.7 is production-ready.
- Qwen3.7-Max-Preview is not yet a production choice.
For Qwen access details, see how to use the Qwen 3.7 API and how to use Qwen 3.7 for free.
Latency
Latency matters for chat UIs and multi-step agents.
Per Artificial Analysis:
| Model | Time to first token | Output speed |
|---|---|---|
| GPT-5.5 xhigh | ~101 s | ~65.9 tok/s |
| Claude Opus 4.7 max | ~27 s | ~49.4 tok/s |
| Qwen3.7-Max-Preview | Not published | Not published |
Interpretation:
- Claude Opus 4.7 starts responding faster.
- GPT-5.5 starts slower in high-effort mode but streams faster once it begins.
- Qwen latency is unknown, but its high output-token volume may increase end-to-end time.
For production, test lower-effort variants if available. Most real applications should not default every request to maximum reasoning effort.
A useful latency test should measure:
- time to first token
- total response time
- output tokens per second
- retries
- timeout rate
- cost per successful response
Full comparison table
| Criterion | Qwen3.7-Max-Preview | GPT-5.5 | Claude Opus 4.7 |
|---|---|---|---|
| Vendor | Alibaba | OpenAI | Anthropic |
| Released | Preview, mid-May 2026 | April 23, 2026 | April 16, 2026 |
| AA Intelligence Index | 57 (#1 / 218 overall) | 60 (highest score) | 57 (#3 in class) |
| LM Arena text Elo | ~1,475 (#14, preliminary) | ~1,478 (#11) | ~1,492 (#4) |
| SWE-bench Verified | Not published | 88.7% | 87.6% |
| SWE-bench Pro | Not published | ~59% | ~64% |
| Context window | 1.0M tokens | 1M API / ~922K effective / 400K Codex | 1.0M tokens |
| Input price (per 1M) | Not announced (Qwen3.6-Max: ~$1.30) | $5.00 | $6.25 |
| Output price (per 1M) | Not announced (Qwen3.6-Max: ~$7.80) | $30.00 | $25.00 |
| Output speed | Not published | ~65.9 tok/s | ~49.4 tok/s |
| Time to first token | Not published | ~101 s (xhigh) | ~27 s |
| Availability | Preview only (Model Studio / Qwen Studio) | GA (OpenAI API, Codex) | GA (Anthropic API, Bedrock, Vertex) |
| Open weights | No (Max proprietary; Plus to be open) | No | No |
| Reasoning model | Yes (extended thinking) | Yes (extended thinking) | Yes (adaptive reasoning) |
Sources: Artificial Analysis model pages, the LM Arena text leaderboard, SWE-bench leaderboard tracking, and vendor announcements, all current as of late May 2026. Preview-stage Qwen figures are not finalized. Benchmark and Elo numbers move, so verify against live boards before quoting them.
Real-world use cases
1. Autonomous coding agent
Pick GPT-5.5 if your agent needs to:
- resolve GitHub issues
- run terminal commands
- call tools repeatedly
- keep token usage low
- operate over long agent loops
GPT-5.5 leads SWE-bench Verified, performs strongly on terminal workflows, and is reported to use far fewer output tokens on equivalent tasks.
Pick Claude Opus 4.7 if your agent works on larger repositories where architecture-level reasoning is more important than shell throughput.
2. Large legacy-codebase refactor
Pick Claude Opus 4.7.
This is where its strengths line up best:
- 1M-token context
- strong SWE-bench Pro performance
- broad-codebase reasoning
- cleaner engineering explanations
Use it when the task involves many files, implicit dependencies, and PR-quality changes.
3. Long-document analysis
This is close because all three models offer roughly 1M-token context.
For production today:
- use Claude Opus 4.7 for cleaner summaries and stronger human preference
- use GPT-5.5 when input cost and automation workflows matter more
For evaluation:
- test Qwen3.7-Max-Preview if you have access and cost is likely to be a major constraint
4. Customer-facing assistants
Pick Claude Opus 4.7.
LM Arena is the most relevant signal here because it measures human preference directly. Claude Opus 4.7 leads the three on that dimension.
GPT-5.5 is still a strong option, especially where faster streaming after first token improves perceived responsiveness.
5. High-volume cost-sensitive workloads
The best answer depends on token mix.
Use this decision pattern:
If input tokens dominate:
test GPT-5.5 first
If output tokens dominate:
test Claude Opus 4.7 first
If Qwen pricing becomes public and production access opens:
benchmark Qwen3.7-Max against both
For classification, extraction, enrichment, and bulk generation, measure real output length. A cheaper token rate can lose if the model emits many more tokens.
Per-use-case picks
| Use case | Best pick |
|---|---|
| Coding agents and terminal automation | GPT-5.5 |
| Large-codebase engineering | Claude Opus 4.7 |
| Conversational products | Claude Opus 4.7 |
| Raw benchmark intelligence | GPT-5.5 |
| Budget long-context evaluation | Qwen3.7-Max-Preview |
| Available-today all-rounder | GPT-5.5 or Claude Opus 4.7 |
If you are also evaluating Google’s model, see what Gemini 3.5 is and the direct Gemini 3.5 vs GPT-5.5 vs Opus 4.7 comparison.
How to test all three yourself
Benchmarks are useful, but your workload is the deciding factor. Test the same prompt against each model and compare:
- answer quality
- correctness
- tool-use behavior
- input tokens
- output tokens
- latency
- retry rate
- total cost
Apidog makes side-by-side API testing straightforward:
- Create one request for each model’s chat endpoint.
- Put all requests in the same workspace.
- Send the same prompt and context to each model.
- Compare responses, token usage, and latency.
- Save the setup as a reusable test scenario.
- Re-run the same comparison when models update.
This avoids switching between separate dashboards, scripts, and logs. You can also download Apidog to set up your first multi-model comparison.
Conclusion
There is no universal winner.
The practical decision is:
- Choose GPT-5.5 for coding agents, terminal automation, high benchmark intelligence, and token-efficient workflows.
- Choose Claude Opus 4.7 for large-codebase engineering, human-preferred responses, and production workloads that need broad cloud availability.
- Choose Qwen3.7-Max-Preview for evaluation, long-context experiments, and cost-sensitive planning, but wait for production API access and pricing before shipping on it.
The “Qwen ranked #1” headline is accurate but incomplete. Qwen tops the overall Artificial Analysis leaderboard, while GPT-5.5 has the higher raw score.
Before committing, run your own benchmark suite against the same prompts in Apidog. A few hours of side-by-side testing will tell you more than a leaderboard alone.
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