Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GLM-4.7 (Reasoning) | Qwen3.6 35B A3B (Reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.6 | $0.38 |
| Output ($/M tokens) | $2.2 | $2.25 |
Verdict. GLM-4.7 (Reasoning) wins the overall benchmark matchup 2–0 across 2 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, GLM-4.7 (Reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GLM-4.7 (Reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. GLM-4.7 (Reasoning) is strongest on Coding Index (45.3), Intelligence Index (34.5). Qwen3.6 35B A3B (Reasoning) leads on Coding Index (41.9), Intelligence Index (32.1).
Speed. On throughput, Qwen3.6 35B A3B (Reasoning) generates tokens at 122 tok/s versus 97 tok/s — about 21% faster. On time-to-first-token, GLM-4.7 (Reasoning) responds in 1320ms vs 2040ms, which matters most for chat-style UIs.
Provider. Z AI and Alibaba sell to overlapping but distinct developer audiences: Z AI tends to ship frontier reasoning models with premium positioning, while Alibaba often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): GLM-4.7 (Reasoning) costs $51.00 ($612/year); Qwen3.6 35B A3B (Reasoning) costs $45.15 ($542/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): GLM-4.7 (Reasoning) ≈ $7.40/run, Qwen3.6 35B A3B (Reasoning) ≈ $6.40/run. At agent/realtime scale (200M input / 100M output per million requests): GLM-4.7 (Reasoning) ≈ $340/run, Qwen3.6 35B A3B (Reasoning) ≈ $301/run. Qwen3.6 35B A3B (Reasoning) becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
Qwen3.6 35B A3B (Reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.85/M tokens vs $1.00/M for GLM-4.7 (Reasoning).
GLM-4.7 (Reasoning) wins 2 out of 12 benchmarks compared to 0 for Qwen3.6 35B A3B (Reasoning). See the detailed benchmark chart above for per-category results.
Qwen3.6 35B A3B (Reasoning) generates tokens faster at 122 tok/s vs 97 tok/s. GLM-4.7 (Reasoning) also has lower time-to-first-token (1.32s vs 2.04s).
Choose based on your priorities: Qwen3.6 35B A3B (Reasoning) for lower cost, GLM-4.7 (Reasoning) for stronger benchmark performance, and Qwen3.6 35B A3B (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.