GLM 5.2 vs Qwen3.7 Max
GLM 5.2 (Alibaba DashScope, 1,048,576-token context) versus Qwen3.7 Max (Alibaba DashScope, 991,808-token context). GLM 5.2 is cheaper by 42% on a blended token mix. Across 2 public benchmarks we tracked, GLM 5.2 wins 1 and Qwen3.7 Max wins 1. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — GLM 5.2 vs Qwen3.7 Max
GLM 5.2 and Qwen3.7 Max target overlapping workloads but differ sharply on economics. GLM 5.2 runs roughly 42% cheaper on a blended input-plus-output token mix, which translates to approximately $5,160 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
Across 2 public benchmarks, GLM 5.2 leads on 1 and Qwen3.7 Max leads on 1. The widest gap is on arena-elo-coding, where Qwen3.7 Max scores 8.0 points higher. Benchmarks are noisy and task-dependent — a model that leads on arena-elo may trail on code generation. The safest approach is to run both models on your own golden set before treating any benchmark as decisive.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: glm-5-2
provider: dashscope
fallback:
model: qwen3-7-max
provider: dashscope
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| GLM 5.2 | Qwen3.7 Max | |
|---|---|---|
| Input price | $1.40/M | $2.50/M |
| Output price | $4.40/M | $7.50/M |
| Context window | 1,048,576 | 991,808 |
| Max output | 131,072 | 65,536 |
| Function calling | ✓ | ✓ |
| Vision | — | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | Oct 22, 2026 | Oct 22, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | GLM 5.2 | Qwen3.7 Max | Delta |
|---|---|---|---|
| Startup 10K requests/day | $684 /mo | $1,200 /mo | $516/mo |
| Mid-market 100K requests/day | $6,840 /mo | $12,000 /mo | $5,160/mo |
| Enterprise 1M requests/day | $68,400 /mo | $120,000 /mo | $51,600/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
You're cost-sensitive at scale — GLM 5.2 runs ~42% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
On arena-elo-coding, Qwen3.7 Max scores 8.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
Benchmark winners — by the numbers
For each public benchmark that has scores for both models, the higher score and the size of the gap. Benchmarks are noisy — treat anything under a 2-point delta as effectively tied.
| Benchmark | GLM 5.2 | Qwen3.7 Max | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1476.0 | 1474.0 | GLM 5.2 | +2.0 |
| arena-elo-coding | 1515.0 | 1523.0 | Qwen3.7 Max | +8.0 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Max output tokens differ: 131,072 on GLM 5.2 vs 65,536 on Qwen3.7 Max. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
How to A/B test GLM 5.2 vs Qwen3.7 Max in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark GLM 5.2 primary, mirror 20% of traffic to Qwen3.7 Max in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — GLM 5.2 vs Qwen3.7 Max
Which is cheaper, GLM 5.2 or Qwen3.7 Max? ▾
GLM 5.2 is cheaper by roughly 42% on a blended input + output token mix. Input prices are $1.40/M for GLM 5.2 versus $2.50/M for Qwen3.7 Max; output prices are $4.40/M versus $7.50/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of GLM 5.2 versus Qwen3.7 Max? ▾
GLM 5.2 supports up to 1,048,576 tokens of context. Qwen3.7 Max supports up to 991,808 tokens. GLM 5.2 has the larger window by a factor of 1.1x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do GLM 5.2 and Qwen3.7 Max both support tool calling? ▾
Yes — both GLM 5.2 and Qwen3.7 Max support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Which model supports prompt caching for cost reduction? ▾
Both GLM 5.2 and Qwen3.7 Max support prompt caching. Cached input tokens are typically discounted 50–90% versus uncached input, depending on the provider. For agents with a stable system prompt + retrieval context, the cached pricing tier is the real unit economics number to track.
When should I choose GLM 5.2 over Qwen3.7 Max? ▾
You're cost-sensitive at scale — GLM 5.2 runs ~42% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
When should I choose Qwen3.7 Max over GLM 5.2? ▾
On arena-elo-coding, Qwen3.7 Max scores 8.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
How do I A/B test GLM 5.2 against Qwen3.7 Max in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.