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Model profile
Zhipu
New in 2026

GLM-5 (Reasoning)

GLM-5 (Reasoning) is a budget text-first model from Zhipu with a balanced runtime profile, unknown context posture, and the clearest fit around agent workflows / reasoning.

Best for: Agent workflows / ReasoningBalanced latencyUnknown contextBudget pricing
Intelligence
49.8

Benchmark blend

Coding
44.2

Dev workflow signal

Context
N/A

Unknown

Input Price
$1.00

Budget tier

Decision snapshot
54

GLM-5 (Reasoning) currently reads as a budget text-first option with unknown context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Reasoning
Latency tier
Balanced
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis
Compare this modelSee leaderboard

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
49.8
50

General reasoning and benchmark headroom.

Situational
Speed
50 tok/s
62

TTFT 0.80s

Competitive
Context
N/A
34

How much prompt and task state can stay in view.

Limited
Price
$1.00
86

$3.20 output / 1M

Efficient

Editorial Profile

GLM-5 (Reasoning) in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 44Math score 36

The GLM-5 (Reasoning) AI model by Zhipu.

Identity

Zhipu text-first profile

Positioning

Agent workflows / Reasoning with unknown context and balanced runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths

  • The available source data suggests a balanced profile rather than one dominant edge.

Tradeoffs

  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit

  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
49.8
GPQA
82.0%
HLE
27.2%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
44.2
SciCode
46.2%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
72.3%
TAU2
98.2%
TerminalBench Hard
43.2%
LCR
63.3%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$1.00
Output
per 1M output tokens
$3.20
Blended
AA 3:1 mix
$1.55

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.