Skip to main content
BenchLM

GLM-4.6 vs GPT-5.6 Terra

Updated September 24, 2026. Rank says GPT-5.6 Terra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Share or export
Share on XLinkedInSocial cardCSVJSON

Decision reading

GPT-5.6 Terra has the higher public score, 72.58 versus 39.8, and the 90% score intervals do not overlap. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Z.AI logo
GLM-4.6

Z.AI

39.8/100

Supported · Public rank #115

90% interval 23.8–55.8

Model B
OpenAI logo
GPT-5.6 Terra

OpenAI

72.58/100

Supported · Public rank #9

90% interval 68.5–76.7

Shared results
5
GLM-4.6 only
1
GPT-5.6 Terra only
27
Like-for-like categories
1 / 8
Supported: GLM-4.6 and GPT-5.6 TerraHow the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    GPT-5.6 Terra

    GPT-5.6 Terra has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    GLM-4.6 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    GLM-4.6 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.6 does not fit this workload in one request. GLM-4.6 has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

27.7GLM-4.664.7GPT-5.6 Terra

Directional only · BenchAlign v5.7

GPT-5.6 Terra scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Knowledge

Like-for-like
GLM-4.6
35.9
Supported · #103/158
GPT-5.6 Terra
71.1
Supported · #9/158
Basis
BenchAlign v5.7 lane · 2 vs 8 public rows
Reading
GPT-5.6 Terra leads

Coding

Directional only
GLM-4.6
27.7
Estimated · #91/135
GPT-5.6 Terra
64.7
Supported · #8/135
Basis
BenchAlign v5.7 lane · 2 vs 8 public rows
Reading
Directional only

Instruction following

Directional only
GLM-4.6
40.7
#99/124
GPT-5.6 Terra
85.7
#35/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
GLM-4.6
Not ranked
GPT-5.6 Terra
59.5
Supported · #18/105
Basis
BenchAlign v5.7 lane · 0 vs 9 public rows
Reading
Not comparable

Reasoning

Not comparable
GLM-4.6
40.2
Unranked · 2 rankable rows
GPT-5.6 Terra
65.4
#12/19
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-4.6
Not ranked
GPT-5.6 Terra
77.2
#16/50
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GLM-4.6
Not ranked
GPT-5.6 Terra
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-4.6
27.3
Unranked · 2 rankable rows
GPT-5.6 Terra
96.8
Unranked · 3 rankable rows
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GLM-4.6
API rate not published
Fits in one request
GPT-5.6 Terra
$0.008
Fits in one request

GLM-4.6 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-4.6
API rate not published
Fits in one request
GPT-5.6 Terra
$0.136
Fits in one request

GLM-4.6 has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GLM-4.6
API rate not published
Does not fit in one request
Cached-input rate unavailable
GPT-5.6 Terra
$0.2
Fits in one request

GLM-4.6 does not fit this workload in one request. GLM-4.6 has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

GLM-4.6

200K

GPT-5.6 Terra

1.05M

OpenAI model catalog

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GLM-4.6

No comparable hosted API rate

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Provider availability

GLM-4.6

Not sourced

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

GLM-4.6

Reasoning

GPT-5.6 Terra

Reasoning

Weight access

GLM-4.6

Open Weight

GPT-5.6 Terra

Proprietary

License

GLM-4.6

Open Weight

GPT-5.6 Terra

Proprietary

Release date

GLM-4.6

2025-09-01

GPT-5.6 Terra

2026-07-09

If you already use one of these models

Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
GPT-5.6 Terra has the higher public score, 72.58 versus 39.8, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GPT-5.6 Terra has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GLM-4.6 or GPT-5.6 Terra?

GPT-5.6 Terra has the higher public score, 72.58 versus 39.8, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GLM-4.6 or GPT-5.6 Terra?

GPT-5.6 Terra scores higher for coding on the public lane, 64.7 to 27.7. GLM-4.6 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GLM-4.6 or GPT-5.6 Terra?

GLM-4.6 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GLM-4.6 or GPT-5.6 Terra?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GLM-4.6 or GPT-5.6 Terra?

GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 200K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence33 rows

Agentic

  • Terminal-Bench 3.0

    GLM-4.6—
    GPT-5.6 Terra20.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    GLM-4.6—
    GPT-5.6 Terra87.4%
    Source

    Not directly comparable

  • BrowseComp

    GLM-4.6—
    GPT-5.6 Terra87.5%
    Source

    Not directly comparable

  • OSWorld 2.0

    GLM-4.6—
    GPT-5.6 Terra50.2%
    Source

    Not directly comparable

  • CyberGym

    GLM-4.6—
    GPT-5.6 Terra81.8%
    Source

    Not directly comparable

  • ExploitGym

    GLM-4.6—
    GPT-5.6 Terra23.2%
    Source

    Not directly comparable

  • Toolathlon

    GLM-4.6—
    GPT-5.6 Terra53.1%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-4.6—
    GPT-5.6 Terra77.5%
    Source

    Not directly comparable

  • ApprenticeBench

    GLM-4.6—
    GPT-5.6 Terra16%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    GLM-4.63.09%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-4.681.0%
    Source
    GPT-5.6 Terra85.9%
    Source

    GPT-5.6 Terra leads this result

  • SWE-bench Pro

    GLM-4.6—
    GPT-5.6 Terra63.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    GLM-4.6—
    GPT-5.6 Terra87.4%
    Source

    Not directly comparable

  • DeepSWE

    GLM-4.6—
    GPT-5.6 Terra69.6%
    Source

    Not directly comparable

  • FrontierCode 1.1 Extended

    GLM-4.6—
    GPT-5.6 Terra55.8%
    Source

    Not directly comparable

  • cursorBench32

    GLM-4.6—
    GPT-5.6 Terra64.9%
    Source

    Not directly comparable

  • VulcanBench v3

    GLM-4.6—
    GPT-5.6 Terra87.0%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    GLM-4.6—
    GPT-5.6 Terra95.4%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GLM-4.6—
    GPT-5.6 Terra83.9%
    Source

    Not directly comparable

  • ARC-AGI-3

    GLM-4.6—
    GPT-5.6 Terra0.8%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GLM-4.6—
    GPT-5.6 Terra80.7%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    GLM-4.6—
    GPT-5.6 Terra82%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    GLM-4.674.5%
    Source
    GPT-5.6 Terra90.9%
    Source

    GPT-5.6 Terra leads this result

  • MMLU-Pro (Vals)

    GLM-4.682.2%
    Source
    GPT-5.6 Terra86.7%
    Source

    GPT-5.6 Terra leads this result

  • GPQA

    GLM-4.6—
    GPT-5.6 Terra92.9%
    Source

    Not directly comparable

  • GPQA-D

    GLM-4.6—
    GPT-5.6 Terra92.9%
    Source

    Not directly comparable

  • HLE-Verified

    GLM-4.6—
    GPT-5.6 Terra51.1%
    Source

    Not directly comparable

  • LABBench2

    GLM-4.6—
    GPT-5.6 Terra81.2%
    Source

    Not directly comparable

  • HealthBench Professional

    GLM-4.6—
    GPT-5.6 Terra57.7%
    Source

    Not directly comparable

  • HealthBench Hard

    GLM-4.6—
    GPT-5.6 Terra32.7%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GLM-4.63.819%
    Source
    GPT-5.6 Terra84.900%
    Source

    GPT-5.6 Terra leads this result

  • FrontierMath v2 (Tier 4)

    GLM-4.62.128%
    Source
    GPT-5.6 Terra68.300%
    Source

    GPT-5.6 Terra leads this result

  • FrontierMath (legacy)

    GLM-4.6—
    GPT-5.6 Terra84.9%
    Source

    Not directly comparable

33 public results · 5 shared

Related comparisons

Compare API pricingRead the methodologyOpen the model selector

Watch GLM-4.6 vs GPT-5.6 Terra

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.

Last updated September 24, 2026