Inkling vs Qwen3.6-27B

Updated October 14, 2026. Rank says Inkling is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Inkling has the higher public point estimate, 52.13 versus 48.43. Their conditional score ranges overlap. These ranges do not establish rank confidence. 9 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Inkling

Thinking Machines Lab

52.13/100

Supported · Public rank #85

90% interval 41.7–62.5

Model B

Qwen3.6-27B

Alibaba

48.43/100

Estimated · Public rank #99

Conditional range 34.1–62.8

Shared results: 9
Inkling only: 15
Qwen3.6-27B only: 31
Like-for-like categories: 3 / 8

Supported: Inkling · Estimated: Qwen3.6-27B. Conditional ranges do not establish rank confidence.How 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.

Coding work
Code generation, repair, and software-engineering tasks
Qwen3.6-27B
Qwen3.6-27B has the higher public coding point estimate, 35.4 to 34, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Inkling
Inkling has the higher public agentic point estimate, 36.2 to 28.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Inkling
Inkling has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

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

Pick the job you would use them for

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

34.0Inkling35.4Qwen3.6-27B

Like-for-like · BenchAlign v5.8

Qwen3.6-27B has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

HLEKnowledge
Normalized gap 22.0
Inkling:46%
Qwen3.6-27B:24%
Inkling source Qwen3.6-27B source
CharXivMultimodal
Normalized gap 3.6
Inkling:82%
Qwen3.6-27B:78.4%
Inkling source Qwen3.6-27B source
AIME26Math
Normalized gap 3.0
Inkling:97.1%
Qwen3.6-27B:94.1%
Inkling source Qwen3.6-27B source
MMMU-ProMultimodal
Normalized gap 2.3
Inkling:73.5%
Qwen3.6-27B:75.8%
Inkling source Qwen3.6-27B source
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 1.8
Inkling:33.333%
Qwen3.6-27B:35.088%
Shared source

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 v5.8 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.

Agentic

Like-for-like

Inkling: 36.2; Supported · #72/124
Qwen3.6-27B: 28.2; Supported · #91/124
Basis: BenchAlign v5.8 lane · 5 vs 6 public rows
Reading: Inkling leads · intervals overlap

Coding

Like-for-like

Inkling: 34.0; Supported · #80/149
Qwen3.6-27B: 35.4; Supported · #76/149
Basis: BenchAlign v5.8 lane · 6 vs 7 public rows
Reading: Qwen3.6-27B leads · intervals overlap

Multimodal

Like-for-like

Inkling: 67.5; #35/54
Qwen3.6-27B: 67.3; #38/54
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Practical tie

Knowledge

Directional only

Inkling: 54.5; Supported · #62/181
Qwen3.6-27B: 46.9; Estimated · #88/181
Basis: BenchAlign v5.8 lane · 6 vs 6 public rows
Reading: Directional only

Instruction following

Directional only

Inkling: 87.6; #35/128
Qwen3.6-27B: 82.4; #52/128
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Directional only

Math

Directional only

Inkling: 58.3; #11/12
Qwen3.6-27B: 65.9; #8/12
Basis: Provisional lane · 3 vs 3 weighted rows
Reading: Directional only

Reasoning

Not comparable

Inkling: 79.0; #14/29
Qwen3.6-27B: 79.0; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Inkling: Not ranked
Qwen3.6-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Inkling and Qwen3.6-27B
Category	Inkling	Qwen3.6-27B	Basis	Reading
Agentic	36.2Supported · #72/124	28.2Supported · #91/124	Like-for-likeBenchAlign v5.8 lane · 5 vs 6 public rows	Inkling leads · intervals overlap
Coding	34.0Supported · #80/149	35.4Supported · #76/149	Like-for-likeBenchAlign v5.8 lane · 6 vs 7 public rows	Qwen3.6-27B leads · intervals overlap
Multimodal	67.5#35/54	67.3#38/54	Like-for-likeProvisional lane · 2 vs 2 weighted rows	Practical tie
Knowledge	54.5Supported · #62/181	46.9Estimated · #88/181	Directional onlyBenchAlign v5.8 lane · 6 vs 6 public rows	Directional only
Instruction following	87.6#35/128	82.4#52/128	Directional onlyProvisional lane · 1 vs 0 weighted rows	Directional only
Math	58.3#11/12	65.9#8/12	Directional onlyProvisional lane · 3 vs 3 weighted rows	Directional only
Reasoning	79.0#14/29	79.0Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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

Inkling: $0.00421; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.6-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Inkling: $0.10754; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.6-27B has no comparable published API token rate.

Cache-heavy agent loop

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

Inkling: $0.159; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Qwen3.6-27B 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.

SpecificationInklingQwen3.6-27B

Context window

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

Inkling

Qwen3.6-27B

262K

API model ID

Inkling

Not sourced

Qwen3.6-27B

Not sourced

Cached-input rate

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

Inkling

$0.374 per 1M cached input tokens

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

Inkling

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

Inkling

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

Inkling

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

Inkling

Hybrid

Qwen3.6-27B

Reasoning

Weight access

Inkling

Open Weight

Qwen3.6-27B

Open Weight

License

Inkling

Open Weight

Qwen3.6-27B

Open Weight

Release date

Inkling

2026-07-15

Qwen3.6-27B

2026-04-21

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: Inkling has the higher public point estimate, 52.13 versus 48.43. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Inkling has the larger documented window (1M).

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

Questions

Which is better, Inkling or Qwen3.6-27B?

Inkling has the higher public point estimate, 52.13 versus 48.43. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Inkling or Qwen3.6-27B?

Qwen3.6-27B has the higher public coding point estimate, 35.4 to 34, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Inkling or Qwen3.6-27B?

Inkling has the higher public agentic tasks point estimate, 36.2 to 28.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Inkling or Qwen3.6-27B?

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, Inkling or Qwen3.6-27B?

Inkling has the larger documented context window: 1M, compared with 262K.

Self-host vs API cost

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

Inkling

API / mo$4,913

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Qwen3.6-27B

API / mo$0

Self-host / mo$429

Break-even—

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 evidence55 rows

Agentic

Terminal-Bench 2.1
Inkling63.8%
Source
Qwen3.6-27B—
Not directly comparable
BrowseComp
Inkling77.1%
Source
Qwen3.6-27B—
Not directly comparable
MCP Atlas
Inkling74.1%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Inkling47.6%
Source
Qwen3.6-27B—
Not directly comparable
CWE-bench v1
Inkling37.0%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.0
Inkling—
Qwen3.6-27B59.3%
Source
Not directly comparable
Claw-Eval
Inkling—
Qwen3.6-27B72.4%
Source
Not directly comparable
QwenClawBench
Inkling—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
Inkling—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
Inkling—
Qwen3.6-27B70.3%
Source
Not directly comparable
Gert Labs
Inkling—
Qwen3.6-27B54.84%
Source
Not directly comparable

Coding

SWE-bench Verified
Inkling77.6%
Source
Qwen3.6-27B77.2%
Source
Inkling leads this result
SWE-bench Pro
Inkling54.3%
Source
Qwen3.6-27B53.5%
Source
Inkling leads this result
Terminal-Bench 2.1
Inkling63.8%
Source
Qwen3.6-27B—
Not directly comparable
FrontierSWE v2
Inkling4.1%
Source
Qwen3.6-27B—
Not directly comparable
LiveCodeBench (Vals)
Inkling85.5%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench (Vals)
Inkling77.6%
Source
Qwen3.6-27B70.0%
Source
Inkling leads this result
SWE Multilingual
Inkling—
Qwen3.6-27B71.3%
Source
Not directly comparable
Terminal-Bench 2.0
Inkling—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench v6
Inkling—
Qwen3.6-27B83.9%
Source
Not directly comparable
NL2Repo
Inkling—
Qwen3.6-27B36.2%
Source
Not directly comparable

Multimodal

MMMU-Pro
Inkling73.5%
Source
Qwen3.6-27B75.8%
Source
Qwen3.6-27B leads this result
CharXiv
Inkling82%
Source
Qwen3.6-27B78.4%
Source
Inkling leads this result
CharXiv w/o tools
Inkling78.1%
Source
Qwen3.6-27B—
Not directly comparable
MMMU
Inkling—
Qwen3.6-27B82.9%
Source
Not directly comparable
RealWorldQA
Inkling—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
Inkling—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
Inkling—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
Inkling—
Qwen3.6-27B56.1%
Source
Not directly comparable
CC-OCR
Inkling—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
Inkling—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
Inkling—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
Inkling—
Qwen3.6-27B62.5%
Source
Not directly comparable
Video-MME (with subtitle)
Inkling—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
Inkling—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
Inkling—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
Inkling—
Qwen3.6-27B94.7%
Source
Not directly comparable

Knowledge

GPQA
Inkling87.9%
Source
Qwen3.6-27B87.8%
Source
Inkling leads this result
GPQA-D
Inkling87.9%
Source
Qwen3.6-27B—
Not directly comparable
HLE
Inkling46%
Source
Qwen3.6-27B24%
Source
Inkling leads this result
HLE w/o tools
Inkling30%
Source
Qwen3.6-27B—
Not directly comparable
GPQA Diamond (Vals)
Inkling87.1%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro (Vals)
Inkling86.3%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro
Inkling—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
Inkling—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
Inkling—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
Inkling—
Qwen3.6-27B91.4%
Source
Not directly comparable

Instruction following

IFBench
Inkling79.8%
Source
Qwen3.6-27B—
Not directly comparable

Math

AIME26
Inkling97.1%
Source
Qwen3.6-27B94.1%
Source
Inkling leads this result
FrontierMath v2 (Tiers 1-3)
Shared source
Inkling33.333%
Qwen3.6-27B35.088%
Qwen3.6-27B leads this result
FrontierMath v2 (Tier 4)
Inkling4.878%
Source
Qwen3.6-27B—
Not directly comparable
HMMT Feb 2025
Inkling—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
Inkling—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
Inkling—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
Inkling—
Qwen3.6-27B80.8%
Source
Not directly comparable

55 public results · 9 shared

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Last updated October 14, 2026

Inkling vs Qwen3.6-27B

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Multimodal

Knowledge

Instruction following

Math

Reasoning

Multilingual

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Inkling or Qwen3.6-27B?

Which is better for coding, Inkling or Qwen3.6-27B?

Which is better for agentic tasks, Inkling or Qwen3.6-27B?

Which costs less, Inkling or Qwen3.6-27B?

Which has the larger context window, Inkling or Qwen3.6-27B?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Inkling vs Qwen3.6-27B