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Compare/Apriel-v1.6-15B-Thinker vs GLM-4.5V (Non-reasoning)

Apriel-v1.6-15B-ThinkervsGLM-4.5V (Non-reasoning)

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

ServiceNow

Apriel-v1.6-15B-Thinker

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
Apriel-v1.6-15B-Thinker
Faster (tok/s)
GLM-4.5V (Non-reasoning)
Lower Latency
GLM-4.5V (Non-reasoning)
Benchmarks (1-0)
Apriel-v1.6-15B-Thinker

Pricing Comparison

MetricApriel-v1.6-15B-ThinkerGLM-4.5V (Non-reasoning)
Input ($/M tokens)$0$0.6
Output ($/M tokens)$0$1.8
Cost for 1M input + 100K output tokens:
Apriel-v1.6-15B-Thinker$0.00
GLM-4.5V (Non-reasoning)$0.78

Speed Comparison

Output Speed (tokens/s) — higher is better
Apriel-v1.6-15B-Thinker
—
GLM-4.5V (Non-reasoning)
42 tok/s
Time to First Token (seconds) — lower is better
Apriel-v1.6-15B-Thinker
—
GLM-4.5V (Non-reasoning)
2.74s

Editorial Analysis

Verdict. Apriel-v1.6-15B-Thinker wins the overall benchmark matchup 1–0 across 1 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. Apriel-v1.6-15B-Thinker is strongest on Intelligence Index (13.4). 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. ServiceNow and Z AI sell to overlapping but distinct developer audiences: ServiceNow 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): Apriel-v1.6-15B-Thinker 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): Apriel-v1.6-15B-Thinker ≈ $0.00/run, GLM-4.5V (Non-reasoning) ≈ $6.60/run. At agent/realtime scale (200M input / 100M output per million requests): Apriel-v1.6-15B-Thinker ≈ $0/run, GLM-4.5V (Non-reasoning) ≈ $300/run. Apriel-v1.6-15B-Thinker 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
13.46.7
Coding Index
——
Math Index
——
GPQA Diamond
——
MMLU-Pro
——
LiveCodeBench
——
AIME 2025
——
MATH-500
——
Humanity's Last Exam
——
SciCode
——
IFBench
——
TerminalBench
——
Apriel-v1.6-15B-Thinker1 wins
0 winsGLM-4.5V (Non-reasoning)

Frequently Asked Questions

Which is cheaper, Apriel-v1.6-15B-Thinker or GLM-4.5V (Non-reasoning)?

Apriel-v1.6-15B-Thinker 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?

Apriel-v1.6-15B-Thinker wins 1 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 Apriel-v1.6-15B-Thinker vs GLM-4.5V (Non-reasoning)?

Choose based on your priorities: Apriel-v1.6-15B-Thinker for lower cost, Apriel-v1.6-15B-Thinker for stronger benchmark performance, and GLM-4.5V (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.