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Compare/G9v3-39A5B vs GLM-4.5V (Non-reasoning)

G9v3-39A5BvsGLM-4.5V (Non-reasoning)

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

AI9Stars

G9v3-39A5B

Input
$0/M
Output
$0/M
Speed
—
TTFT
—
Z AI

GLM-4.5V (Non-reasoning)

Input
$0.6/M
Output
$1.8/M
Speed
42 tok/s
TTFT
2.74s

Winner by Category

Cheaper
G9v3-39A5B
Faster (tok/s)
GLM-4.5V (Non-reasoning)
Lower Latency
GLM-4.5V (Non-reasoning)
Benchmarks (2-0)
G9v3-39A5B

Pricing Comparison

MetricG9v3-39A5BGLM-4.5V (Non-reasoning)
Input ($/M tokens)$0$0.6
Output ($/M tokens)$0$1.8
Cost for 1M input + 100K output tokens:
G9v3-39A5B$0.00
GLM-4.5V (Non-reasoning)$0.78

Speed Comparison

Output Speed (tokens/s) — higher is better
G9v3-39A5B
—
GLM-4.5V (Non-reasoning)
42 tok/s
Time to First Token (seconds) — lower is better
G9v3-39A5B
—
GLM-4.5V (Non-reasoning)
2.74s

Editorial Analysis

Verdict. G9v3-39A5B wins the overall benchmark matchup 2–0 across 2 overlapping categories, but raw benchmark score is only one input to the decision.

Pricing. Pricing varies significantly between these models — check the table above for the exact per-token rates. Many production workloads actually surface input-token cost (retrieval-augmented prompts, code-context windows), so factor both directions.

Strengths. G9v3-39A5B is strongest on Coding Index (33.1), Intelligence Index (21.8). GLM-4.5V (Non-reasoning) leads on Intelligence Index (6.7).

Speed. Speed data is incomplete for this pair; benchmark and price should decide.

Provider. AI9Stars and Z AI sell to overlapping but distinct developer audiences: AI9Stars tends to ship frontier reasoning models with premium positioning, while Z AI 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): G9v3-39A5B costs $0.00 ($0/year); GLM-4.5V (Non-reasoning) costs $45.00 ($540/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): G9v3-39A5B ≈ $0.00/run, GLM-4.5V (Non-reasoning) ≈ $6.60/run. At agent/realtime scale (200M input / 100M output per million requests): G9v3-39A5B ≈ $0/run, GLM-4.5V (Non-reasoning) ≈ $300/run. G9v3-39A5B 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
21.86.7
Coding Index
33.1—
Math Index
——
GPQA Diamond
——
MMLU-Pro
——
LiveCodeBench
——
AIME 2025
——
MATH-500
——
Humanity's Last Exam
——
SciCode
——
IFBench
——
TerminalBench
——
G9v3-39A5B2 wins
0 winsGLM-4.5V (Non-reasoning)

Frequently Asked Questions

Which is cheaper, G9v3-39A5B or GLM-4.5V (Non-reasoning)?

G9v3-39A5B is cheaper overall. Its blended price (3:1 input/output ratio) is $0.00/M tokens vs $0.90/M for GLM-4.5V (Non-reasoning).

Which model performs better on benchmarks?

G9v3-39A5B wins 2 out of 12 benchmarks compared to 0 for GLM-4.5V (Non-reasoning). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

GLM-4.5V (Non-reasoning) generates tokens faster at 42 tok/s vs — tok/s. However, GLM-4.5V (Non-reasoning) has lower time-to-first-token (2.74s vs —s).

When should I use G9v3-39A5B vs GLM-4.5V (Non-reasoning)?

Choose based on your priorities: G9v3-39A5B for lower cost, G9v3-39A5B for stronger benchmark performance, and GLM-4.5V (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.