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GLM-5 (Reasoning)

EstablishedReleased Mar 1, 2026Open WeightReasoning200K context

Released Mar 1, 2026 see all recent releases

Decision reading
GLM-5 (Reasoning) scores 60.9 out of 100 and ranks #70 of 231. This profile shows 1 source-displayable benchmark rows. API pricing is $1 input and $3.2 output per million tokens.

Data as of September 3, 2026 · How the score is built

Strongest published evidence

Published rows are visible, but no category has enough eligible evidence for a comparative rank.

Validate before choosing

1 published rows leave some tracked benchmark slots empty. Independent runtime speed has not been measured.

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

60.9/100

field median 59

#70 of 231 ranked models

Price

$1input / $3.20 output

input median $1

blended $2.10

Speed

Not measured

field median 91 tok/s

Time to first token not measured

Context

200Ktokens

field median 256,000

Reported for this model; direct source link not stored

What it costs to get this score

Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.

Explore all models

The chart opens on the current model. Scroll horizontally to inspect the full price axis.

Current modelGLM-5 (Reasoning) · 60.9 score · $2.10 blended per million tokens
405060708090$0.50$1$5$10$25↘ frontierGLM-5 (Reasoning)

Horizontal: blended price per million tokens, log scale · Vertical: public score

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. AgenticNot measured
  2. Coding1/1 verified
  3. ReasoningNot measured
  4. KnowledgeNot measured
  5. MathNot measured
  6. MultilingualNot measured
  7. MultimodalNot measured
  8. Inst. FollowingNot measured
Verified sourceProvisionalNot measured

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

API model ID
Not published
Context window
200K
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
Not sourced yet
Output modalities
Not sourced yet
Parameters
Not sourced yet
Availability
Not sourced yet
Cloud regions
Not tracked yet
Lifecycle
Established
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Not documented in the pricing record
Self-host
Open weights available; hardware estimate not sourced
Rate limits
Not tracked yet

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.

Estimate VRAM from known parameters

Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticWeight 22%0 benchmarksNot measuredNot measured
CodingRank Not rankedWeight 20%1 benchmarkVerified55.8
ReasoningWeight 17%0 benchmarksNot measuredNot measured
KnowledgeWeight 12%0 benchmarksNot measuredNot measured
MathWeight 5%0 benchmarksNot measuredNot measured
MultilingualWeight 7%0 benchmarksNot measuredNot measured
MultimodalWeight 12%0 benchmarksNot measuredNot measured
Inst. FollowingWeight 5%0 benchmarksNot measuredNot measured

Benchmark ledger

Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Coding1 row
Coding benchmark values, best verified comparison, weight, and source status
Vibe Code BenchVibe Code Bench v1.1Score23.36%Versus best verified row

Best verified: Claude Opus 4.7 · 71.00%

Gap47.6 behindWeightDisplay only

Lineage

The sequence follows explicit supersedes links. A successor's displayed score stays at least 0.1 points above its predecessor; raw benchmark rows do not move. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Mar 1, 2026 · you are here

    GLM-5 (Reasoning)

    Score 60.9 · $1 / $3.2

Reasoning

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

GLM-5 (Reasoning) ranks #70 of 231 on the public leaderboard with a score of 60.92/100. It does not yet have enough sourced coverage for a verified position.

GLM-5 (Reasoning) is a open weight model with a 200K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

GLM-5 (Reasoning) sits in the GLM-5 family with GLM-5, GLM-5.2, GLM-5.1, GLM-5.3, GLM-5.3-Flash, GLM-5-Turbo, GLM-5V-Turbo. 1 of 417 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Frequently asked questions

How does GLM-5 (Reasoning) perform overall in AI benchmarks?

GLM-5 (Reasoning) ranks #70 out of 231 models on the public BenchAlign leaderboard, with a score of 60.92/100. Its evidence status is Estimated, and this profile shows 1 source-displayable benchmark row. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is GLM-5 (Reasoning) good for coding and programming?

GLM-5 (Reasoning) has source-displayable benchmark coverage for coding and programming, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is GLM-5 (Reasoning) open source?

GLM-5 (Reasoning) is an open-weight model from Z.AI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.

Which sibling models are related to GLM-5 (Reasoning)?

GLM-5 (Reasoning) belongs to the GLM-5 family. Related tracked variants include GLM-5, GLM-5.2, GLM-5.1, plus 4 more. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.

Does GLM-5 (Reasoning) have full benchmark coverage on BenchLM?

No. GLM-5 (Reasoning) currently has 2 source-displayable rows across 417 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of GLM-5 (Reasoning)?

GLM-5 (Reasoning) has a reported context window of 200K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.

Compare GLM-5 (Reasoning) with every tracked model410 comparisons

Last updated September 3, 2026. Runtime fields remain blank until a sourced snapshot exists.

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