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BenchLM
Z.AI logo
Model A
GLM-5.1

Z.AI

67.04/100

Supported · Public rank #30

90% interval 56.6–77.5

GLM-5.1 vs Inkling

Updated August 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Thinking Machines Lab logo
Model B
Inkling

Thinking Machines Lab

67.02/100

Supported · Public rank #31

90% interval 60.0–74.0

Decision reading

GLM-5.1 has the higher public score estimate, 67.04 versus 67.02, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

7 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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.

  • Agentic work

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

    Inkling

    Inkling leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Inkling

    Inkling has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GLM-5.1

    GLM-5.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    GLM-5.1

    GLM-5.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • 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-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

What is actually comparable

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

Shared results
7
GLM-5.1 only
14
Inkling only
8
Like-for-like categories
1 / 8

3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Like-for-like
GLM-5.1
65.4
Inkling
69.4
Weighted basis
2 vs 2 rows
Reading
Inkling leads

Coding

Directional only
GLM-5.1
61.3
Inkling
68.6
Weighted basis
2 vs 2 rows
Reading
Directional only

Knowledge

Directional only
GLM-5.1
52.3
Inkling
51.6
Weighted basis
1 vs 2 rows
Reading
Directional only

Math

Directional only
GLM-5.1
62.0
Inkling
97.1
Weighted basis
4 vs 1 rows
Reading
Directional only

Reasoning

Not comparable
GLM-5.1
Not measured
Inkling
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.1
Not measured
Inkling
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.1
Not measured
Inkling
76.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.1
Not measured
Inkling
79.8
Weighted basis
0 vs 1 rows
Reading
Not comparable

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.

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-5.1
$0.0036
Fits in one request
Inkling
$0.00421
Fits in one request

GLM-5.1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GLM-5.1
$0.0832
Fits in one request
Inkling
$0.10754
Fits in one request

GLM-5.1 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GLM-5.1
$0.352
Does not fit in one request
Cached input priced at the published list-input rate
Inkling
$0.159
Fits in one request

GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.

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-5.1

203K

Inkling

1M

API model ID

GLM-5.1

Not sourced

Inkling

Not sourced

Cached-input rate

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

GLM-5.1

Not published

Inkling

$0.374 per 1M cached input tokens

Documented inputs

GLM-5.1

Not sourced

Inkling

Not sourced

Documented outputs

GLM-5.1

Not sourced

Inkling

Not sourced

Provider availability

GLM-5.1

Not sourced

Inkling

Not sourced

Reasoning profile

GLM-5.1

Reasoning

Inkling

Hybrid

Weight access

GLM-5.1

Open Weight

Inkling

Open Weight

License

GLM-5.1

Open Weight

Inkling

Open Weight

Release date

GLM-5.1

2026-04-07

Inkling

2026-07-15

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
GLM-5.1 has the higher public score estimate, 67.04 versus 67.02, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0832 vs $0.10754. Cache-heavy agent loop: $0.352 vs $0.159.
Context tradeoff
Inkling has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
Inkling
API / mo$4,913
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence29 rows

Agentic

  • Terminal-Bench 2.0

    GLM-5.163.5%
    Source
    Inkling63.8%
    Source

    Inkling leads this result

  • BrowseComp

    GLM-5.168%
    Source
    Inkling77.1%
    Source

    Inkling leads this result

  • τ³-bench results

    GLM-5.170.6%
    Source
    Inkling—

    Not directly comparable

  • MCP Atlas

    GLM-5.171.8%
    Source
    Inkling74.1%
    Source

    Inkling leads this result

  • CyberGym

    GLM-5.168.7%
    Source
    Inkling—

    Not directly comparable

  • Claw-Eval

    GLM-5.162.3%
    Source
    Inkling—

    Not directly comparable

  • Gert Labs

    GLM-5.160.11%
    Source
    Inkling—

    Not directly comparable

  • ResearchClawBench

    GLM-5.118.2%
    Source
    Inkling—

    Not directly comparable

Coding

  • SWE-bench Pro

    GLM-5.158.4%
    Source
    Inkling54.3%
    Source

    GLM-5.1 leads this result

  • NL2Repo

    GLM-5.142.7%
    Source
    Inkling—

    Not directly comparable

  • SWE-Rebench

    GLM-5.162.7%
    Source
    Inkling—

    Not directly comparable

  • Vibe Code Bench

    GLM-5.131.46%
    Source
    Inkling—

    Not directly comparable

  • OpenHarmony Bench

    GLM-5.152.3%
    Source
    Inkling—

    Not directly comparable

  • SWE-bench Verified

    GLM-5.1—
    Inkling77.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.1—
    Inkling63.8%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    GLM-5.186.2%
    Source
    Inkling87.9%
    Source

    Inkling leads this result

  • HLE

    GLM-5.152.3%
    Source
    Inkling46%
    Source

    GLM-5.1 leads this result

  • GPQA

    GLM-5.1—
    Inkling87.9%
    Source

    Not directly comparable

  • HLE w/o tools

    GLM-5.1—
    Inkling30%
    Source

    Not directly comparable

Math

  • AIME26

    GLM-5.195.3%
    Source
    Inkling97.1%
    Source

    Inkling leads this result

  • HMMT Nov 2025

    GLM-5.194.0%
    Source
    Inkling—

    Not directly comparable

  • HMMT Feb 2026

    GLM-5.182.6%
    Source
    Inkling—

    Not directly comparable

  • MMAnswerBench

    GLM-5.183.8%
    Source
    Inkling—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GLM-5.133.448%
    Source
    Inkling—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GLM-5.112.500%
    Source
    Inkling—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GLM-5.1—
    Inkling73.5%
    Source

    Not directly comparable

  • CharXiv

    GLM-5.1—
    Inkling82%
    Source

    Not directly comparable

  • CharXiv w/o tools

    GLM-5.1—
    Inkling78.1%
    Source

    Not directly comparable

Instruction following

  • IFBench

    GLM-5.1—
    Inkling79.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GLM-5.1 or Inkling?

GLM-5.1 has the higher public score estimate, 67.04 versus 67.02, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GLM-5.1 or Inkling?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GLM-5.1 or Inkling?

Inkling leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.

Which costs less, GLM-5.1 or Inkling?

For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.00421 on Inkling; repository review costs $0.0832 and $0.10754; the cache-heavy agent loop costs $0.352 and $0.159. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GLM-5.1 or Inkling?

Inkling has the larger documented context window: 1M, compared with 203K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 29, 2026

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