Model profile
z-ai
New in 2026

Z.ai: GLM 4.7 Flash

Z.ai: GLM 4.7 Flash is a budget text-first model from z-ai with a fast runtime profile, large context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsFast latencyLarge contextBudget pricing
Intelligence
22.1

Benchmark blend

Coding
11.0

Dev workflow signal

Context
203K Tokens

Large

Input Price
$0.07

Budget tier

Decision snapshot
54

Z.ai: GLM 4.7 Flash currently reads as a budget text-first option with large context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

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
22.1
22

General reasoning and benchmark headroom.

Limited
Speed
188 tok/s
94

TTFT 0.76s

Above average
Context
203K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$0.07
86

$0.40 output / 1M

Efficient

Editorial Profile

Z.ai: GLM 4.7 Flash 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 11Math score 36

As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning, and tool collaboration, and has achieved leading performance among open-source models of the same size on several current public benchmark leaderboards.

Identity

z-ai text-first profile

Positioning

Long-context research / Agent workflows with large context and fast runtime.

Cost posture

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

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • Latency and throughput look responsive enough for interactive loops.

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

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

Best fit
  • Long-context summarization, repo analysis, and policy or document review.

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Context
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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
22.1
GPQA
45.2%
HLE
4.9%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
11.0
SciCode
25.5%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
46.3%
TAU2
91.8%
TerminalBench Hard
3.8%
LCR
14.7%

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
203K Tokens
Vision
Text-first
Modalities
text->text, text
Tokenizer
Other
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoningmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.07
Output
per 1M output tokens
$0.40
Blended
AA 3:1 mix
$0.15

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

OR Cache Read
$0.00

Metadata

Raw source tables at the end

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