Glm 5

Name: Glm 5 — independent benchmark scores
Creator: Epoch AI
License: https://creativecommons.org/licenses/by/4.0/

OpenRouterChat203K context· released 2026-02-11Floating alias

openrouter/z-ai/glm-5

Estimate cost Compare

Prices as of 2026-07-14 · rev 179affb· unknown is shown as unknown, never $0.00

Strongest atReasoningtop-quartile among comparable models scored on these domains

Value position83rd pctile GPQA1 cheaper model matches or beat it on GPQA — see the value chart

Data confidence7/7 exactbenchmarks measured on this exact model · prices 2026-07-14

Cost / request$0.002081K in · 500 out

Input / M$0.80

Output / M$2.56

Context203K202,752 tokens

Max output128K128,000 tokens

Capability fingerprint

independent benchmarks, grouped by domain · higher is better · not our measurement

Reasoning

GPQA Diamondgraduate-level science reasoning

84%83rd pctile

exact modeloptimized runEpoch evaluations · GLM-5

SimpleBencheveryday reasoning (unsaturated)

44%64th pctile

exact modelSimpleBench Leaderboard · GLM-5

Mathematics

AIME (OTIS mock)olympiad math

80%62nd pctile

exact modeloptimized runEpoch evaluations · GLM-5

FrontierMathresearch-level math

29%73rd pctile

exact modeloptimized runEpoch evaluations · GLM-5

Code & agentic

SWE-Bench Verifiedresolves real GitHub issues (agentic)

72%38th pctile

exact modeloptimized runEpoch evaluations · GLM-5

Terminal-Benchautonomous terminal tasks (agentic)

52%69th pctile

exact modeloptimized runEpoch AI evaluations · GLM-5

WeirdMLnovel out-of-distribution ML coding

48%65th pctile

exact modelWeirdML Leaderboard · GLM-5

peer median · percentile ranks this model among distinct comparable models (deduped — one entry per model, not per provider) scored on that benchmark · exact model / family proxy per row.

Source: Epoch AI — “AI Benchmarking Hub” (epoch.ai), CC-BY (Epoch AI). Some rows carry an upstream leaderboard (shown per row); Aider & Terminal-Bench are additionally Apache-2.0. Each benchmark measures a different thing on a different scale — rows are not comparable across domains, and a score is not a guarantee of quality on your task.

Capabilities Index

Epoch AI's cross-model capability re-fit · one comparable axis · not our measurement

146.4ECI

68th percentile of 150 modelsexact model

The dot is Glm 5; the whisker is its 95% CI; the firm tick is the peer median; faint ticks are every other scored model. Unlike a raw benchmark, the ECI axis is comparable across models — but it's a relative re-fit scale, so a model's exact position can shift when Epoch recomputes it.

Model facts · Epoch AI, likely confidence

Developer: Z.ai (Zhipu AI)
First published: 2026-02-17
Parameters: 744B params"GLM-5 scales to 256 experts and reduces its layer count to 80 to minimize expert parallelism communication overhead. This results in a 744B parameter model"
Training compute: 6.8e24 FLOP
Access: Open weights (unrestricted)

Source: Epoch AI — Capabilities Index & Notable-Models data (epoch.ai), CC-BY (Epoch AI). The ECI is Epoch's index, not TokenTriage's; per-fact confidence labels are Epoch's own.

Human preference (LMArena)

blind pairwise human votes · Elo · style-controlled · not a correctness score

exact modelmeasured on glm-5statistically tied with 9 models overall

Each dot is Glm 5's Arena score in that category; the whisker is the 95% CI; the firm tick is the peer median across distinct models; faint ticks are the field. This is human preference — which answer people pick in a blind A/B — not a correctness or capability score, and adjacent ranks routinely overlap within their intervals.

Source: LMArena (LMArena Leaderboard (lmarena.ai) — lmarena-ai/leaderboard-dataset on Hugging Face, CC BY 4.0.). Ratings are the style-controlled estimates (length/formatting confound removed), as of 2026-07-27.

Cost vs quality

where Glm 5 sits among GPQA-scored models

Each dot is a model with a GPQA Diamond score, priced at a 1K-in / 500-out request; Glm 5 is highlighted. The shaded corner holds models that are both cheaper and higher-scoring; the dashed staircase is the value frontier. Hover any dot for its model.

1 cheaper model matches or beats Glm 5 on GPQA— see the full table below, switchable by benchmark.

Also worth considering

cheaper models that score about as well — pick the benchmark that matters to you

2 cheaper models score within 3 points of Glm 5 on GPQA Diamond.

Model	GPQA Diamond	Cost / req	Savings
DEDeepseek V4 Pro	86%+2	$0.00087	−58%
OPQwen3.6 Plus	83%-1	$0.0013	−38%

Deduped to one entry per model (cheapest offering), priced at the reference 1K-in / 500-out request. Filtered only on the benchmark score — a model with an unknown capability is never silently excluded. A lower price is only cheaper if quality holds on your task — that's what routing proves.

Pricing

Input · fresh prompt tokens	$0.80 / M
Output · generated tokens	$2.56 / M
Cache read · cached-input hit	unknown
Cache write · cache creation	unknown
Reasoning · thinking tokens	unknown

Reasoning tokens: no separate rate is published. Providers typically bill thinking tokens at the output rate ($2.56 / M) — which is what the estimator assumes, so an extended-thinking workload isn't silently undercounted.

Model info

Provider: OpenRouter
Catalog key: openrouter/openrouter/z-ai/glm-5
Identifier: Floating alias (may re-point over time)
Type: Chat
Max input: 202,752 tokens
Max output: 128,000 tokens
Released: 2026-02-11
Knowledge cutoff: unknown
Weights: Open weights
Price snapshot: 2026-07-14 · rev 179affb

Pricing from TokenTriage's resolved price artifact (TokenTriage resolved price artifact (internal/pricing/data/prices.json)); capability + model metadata is third-party (LiteLLM model_prices_and_context_window.json, models.dev). Unknown means unstated, not absent.

Capabilities

2 documented as supported

Input & output

✕Visionmodels.dev

?PDF input

?Audio input

?Audio output

API behaviour

?Streaming

?Structured output

?Prompt caching

✓Reasoning

Agents & tools

✓Function calling

?Parallel tool calls

?Web search

?Computer use

✓ supported ·✕ not supported ·? not documented ·⚠ sources disagree. A models.dev tag means the LiteLLM catalog was silent and an independent source supplied the value (lower confidence); ? is never silently turned into a confident ✕.

Open weights

verified Hugging Face repo · facts from the model's safetensors index

Parameters: 753.9B
Architecture: GlmMoeDsaForCausalLM
Native precision: BF16

Native size: 1508 GB

License: mitCommercial use OK
Repository: zai-org/GLM-5

Source: Hugging Face Hub — facts read from the model repo as of 2026-07-30. Each fact carries the model's own repo license (shown above); TokenTriage neither hosts nor relicenses the weights.

Estimate a request

disjoint token buckets · input = fresh (uncached) tokens

Preset

Input tokens

Output tokens

Cached input

Cache write

Reasoning

Requests / month

Cost / request—

Projected / month—

How output length drives the bill

at a 1K-token prompt · cost scales linearly with output

Output tokens overtake input spend at ~312 tokens for this model — past that, generation is your bill, whatever the headline input price says. Computed by the same cost engine as the estimate above.

Compare Glm 5 with other models →

Route to Glm 5

OpenAI-compatible · one base URL

curl https://YOUR-TOKENTRIAGE-HOST/v1/chat/completions \
  -H "Authorization: Bearer $TOKENTRIAGE_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openrouter/openrouter/z-ai/glm-5",
    "messages": [{"role": "user", "content": "Hello"}]
  }'from openai import OpenAI

client = OpenAI(
    base_url="https://YOUR-TOKENTRIAGE-HOST/v1",
    api_key="YOUR_TOKENTRIAGE_KEY",
)

resp = client.chat.completions.create(
    model="openrouter/openrouter/z-ai/glm-5",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

Point your existing OpenAI client at your TokenTriage host and pass the catalog key as the model. TokenTriage attributes the real cost per request and can route to a cheaper model once it's proven just as good on your traffic.

Data & API

machine-readable · rev 179affb

Pull this model's pricing, capabilities, benchmarks and cross-source provenance programmatically — every field carries the snapshot rev, and unknown is null, never 0. Free to reuse with attribution: benchmarks & Capabilities Index © Epoch AI (CC-BY (Epoch AI)); human-preference Elo © LMArena (CC-BY-4.0); capability & price cross-check via models.dev v2 (MIT) · Portkey (MIT) · TrueFoundry (MIT); open-weights facts via Hugging Face (per-repo license).

openrouter-openrouter-z-ai-glm-5.json openrouter-openrouter-z-ai-glm-5.csv

A price is a guess until it meets your traffic.

This page tells you what Glm 5 charges per token. TokenTriage tells you what it costs on your real requests — and cuts the bill only after proving a cheaper model is just as good.

See the product Back to directory