Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GLM-4.6V (Reasoning) | Qwen3 235B A22B 2507 Instruct |
|---|---|---|
| Input ($/M tokens) | $0.3 | $0.23 |
| Output ($/M tokens) | $0.9 | $0.92 |
Verdict. Qwen3 235B A22B 2507 Instruct takes the aggregate benchmark matchup 1–0 across 1 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, GLM-4.6V (Reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GLM-4.6V (Reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. GLM-4.6V (Reasoning) is strongest on Intelligence Index (16.9). Qwen3 235B A22B 2507 Instruct leads on Intelligence Index (18.4).
Speed. Throughput is comparable — 70 tok/s vs 56 tok/s — so generation speed shouldn't drive your choice here. Look at the per-benchmark wins instead.
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-4.6V (Reasoning) costs $22.50 ($270/year); Qwen3 235B A22B 2507 Instruct costs $20.70 ($248/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): GLM-4.6V (Reasoning) ≈ $3.30/run, Qwen3 235B A22B 2507 Instruct ≈ $2.99/run. At agent/realtime scale (200M input / 100M output per million requests): GLM-4.6V (Reasoning) ≈ $150/run, Qwen3 235B A22B 2507 Instruct ≈ $138/run. Qwen3 235B A22B 2507 Instruct 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 235B A22B 2507 Instruct is cheaper overall. Its blended price (3:1 input/output ratio) is $0.40/M tokens vs $0.45/M for GLM-4.6V (Reasoning).
Qwen3 235B A22B 2507 Instruct wins 1 out of 12 benchmarks compared to 0 for GLM-4.6V (Reasoning). See the detailed benchmark chart above for per-category results.
GLM-4.6V (Reasoning) generates tokens faster at 70 tok/s vs 56 tok/s. However, Qwen3 235B A22B 2507 Instruct has lower time-to-first-token (2.39s vs 3.50s).
Choose based on your priorities: Qwen3 235B A22B 2507 Instruct for lower cost, Qwen3 235B A22B 2507 Instruct for stronger benchmark performance, and GLM-4.6V (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.