Compare/GLM-4.5-Air vs Qwen3 Omni 30B A3B Instruct

GLM-4.5-AirvsQwen3 Omni 30B A3B Instruct

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

Z AI

GLM-4.5-Air

Input
$0.17/M
Output
$0.98/M
Speed
84 tok/s
TTFT
2.57s
Alibaba

Qwen3 Omni 30B A3B Instruct

Input
$0.25/M
Output
$0.97/M
Speed
92 tok/s
TTFT
1.92s

Winner by Category

Cheaper
GLM-4.5-Air
Faster (tok/s)
Qwen3 Omni 30B A3B Instruct
Lower Latency
Qwen3 Omni 30B A3B Instruct
Benchmarks (1-0)
GLM-4.5-Air

Pricing Comparison

MetricGLM-4.5-AirQwen3 Omni 30B A3B Instruct
Input ($/M tokens)$0.17$0.25
Output ($/M tokens)$0.98$0.97
Cost for 1M input + 100K output tokens:
GLM-4.5-Air$0.27
Qwen3 Omni 30B A3B Instruct$0.35

Speed Comparison

Output Speed (tokens/s) — higher is better
GLM-4.5-Air
84 tok/s
Qwen3 Omni 30B A3B Instruct
92 tok/s
Time to First Token (seconds) — lower is better
GLM-4.5-Air
2.57s
Qwen3 Omni 30B A3B Instruct
1.92s

Editorial Analysis

Verdict. GLM-4.5-Air wins the overall benchmark matchup 1–0 across 1 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, Qwen3 Omni 30B A3B Instruct is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3 Omni 30B A3B Instruct makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. GLM-4.5-Air is strongest on Intelligence Index (16.7). Qwen3 Omni 30B A3B Instruct leads on Intelligence Index (4.8).

Speed. Throughput is comparable — 84 tok/s vs 92 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-4.5-Air costs $19.80 ($238/year); Qwen3 Omni 30B A3B Instruct costs $22.05 ($265/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): GLM-4.5-Air ≈ $2.81/run, Qwen3 Omni 30B A3B Instruct ≈ $3.19/run. At agent/realtime scale (200M input / 100M output per million requests): GLM-4.5-Air ≈ $132/run, Qwen3 Omni 30B A3B Instruct ≈ $147/run. GLM-4.5-Air 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.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
16.74.8
Coding Index
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
GLM-4.5-Air1 wins
0 winsQwen3 Omni 30B A3B Instruct

Frequently Asked Questions

Which is cheaper, GLM-4.5-Air or Qwen3 Omni 30B A3B Instruct?

GLM-4.5-Air is cheaper overall. Its blended price (3:1 input/output ratio) is $0.37/M tokens vs $0.43/M for Qwen3 Omni 30B A3B Instruct.

Which model performs better on benchmarks?

GLM-4.5-Air wins 1 out of 12 benchmarks compared to 0 for Qwen3 Omni 30B A3B Instruct. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Qwen3 Omni 30B A3B Instruct generates tokens faster at 92 tok/s vs 84 tok/s. However, Qwen3 Omni 30B A3B Instruct has lower time-to-first-token (1.92s vs 2.57s).

When should I use GLM-4.5-Air vs Qwen3 Omni 30B A3B Instruct?

Choose based on your priorities: GLM-4.5-Air for lower cost, GLM-4.5-Air for stronger benchmark performance, and Qwen3 Omni 30B A3B Instruct for faster generation. For latency-sensitive apps, check the TTFT comparison above.