Model A

xAI

67.01/100

Supported · Public rank #34

90% interval 58.4–75.7

Grok 4.20 vs Qwen3.6-27B

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.6-27B

Alibaba

52.73/100

Estimated · Public rank #120

90% interval 47.0–58.5

Decision reading

Grok 4.20 has the higher public score estimate, 67.01 versus 52.73, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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 leads on the public coding lane, 42.6 to 28.2, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Qwen3.6-27B
Qwen3.6-27B leads on the public agentic lane, 33.9 to 26.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Grok 4.20
Grok 4.20 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: rate-fallback
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

What is actually comparable

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

15 Grok 4.20 only

8 Shared

30 Qwen3.6-27B only

Shared results: 8
Grok 4.20 only: 15
Qwen3.6-27B only: 30
Like-for-like categories: 3 / 8

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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

Grok 4.20: 26.5; Supported · #144/151
Qwen3.6-27B: 33.9; Supported · #133/151
Basis: BenchAlign lane · 4 vs 6 public rows
Reading: Qwen3.6-27B leads · intervals overlap

Coding

Like-for-like

Grok 4.20: 28.2; Supported · #171/183
Qwen3.6-27B: 42.6; Supported · #128/183
Basis: BenchAlign lane · 6 vs 6 public rows
Reading: Qwen3.6-27B leads · intervals overlap

Multimodal

Like-for-like

Grok 4.20: 34.1; #43/48
Qwen3.6-27B: 51.5; #35/48
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Qwen3.6-27B leads

Knowledge

Directional only

Grok 4.20: 48.8; Supported · #96/181
Qwen3.6-27B: 49.0; Estimated · #95/181
Basis: BenchAlign lane · 6 vs 6 public rows
Reading: Directional only

Reasoning

Not comparable

Grok 4.20: 34.2; Unranked · 2 rankable rows
Qwen3.6-27B: 73.7; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Grok 4.20: Not ranked
Qwen3.6-27B: 72.8; Unranked · 5 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Grok 4.20: Not ranked
Qwen3.6-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Grok 4.20: Not ranked
Qwen3.6-27B: 82.2; #50/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Grok 4.20 and Qwen3.6-27B
Category	Grok 4.20	Qwen3.6-27B	Basis	Reading
Agentic	26.5Supported · #144/151	33.9Supported · #133/151	Like-for-likeBenchAlign lane · 4 vs 6 public rows	Qwen3.6-27B leads · intervals overlap
Coding	28.2Supported · #171/183	42.6Supported · #128/183	Like-for-likeBenchAlign lane · 6 vs 6 public rows	Qwen3.6-27B leads · intervals overlap
Multimodal	34.1#43/48	51.5#35/48	Like-for-likeProvisional lane · 2 vs 2 weighted rows	Qwen3.6-27B leads
Knowledge	48.8Supported · #96/181	49.0Estimated · #95/181	Directional onlyBenchAlign lane · 6 vs 6 public rows	Directional only
Reasoning	34.2Unranked · 2 rankable rows	73.7Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Math	Not ranked	72.8Unranked · 5 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	82.2#50/120	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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

CharXiv
Multimodal
Grok 4.20: 60.9%Qwen3.6-27B: 78.4%Normalized gap 17.5Grok 4.20 source Qwen3.6-27B source
Terminal-Bench 2.0
Agentic
Grok 4.20: 47.1%Qwen3.6-27B: 59.3%Normalized gap 12.2Grok 4.20 source Qwen3.6-27B source
SWE-bench Pro
Coding
Grok 4.20: 51.8%Qwen3.6-27B: 53.5%Normalized gap 1.7Grok 4.20 source Qwen3.6-27B source
MMMU-Pro
Multimodal
Grok 4.20: 75.2%Qwen3.6-27B: 75.8%Normalized gap 0.6Grok 4.20 source Qwen3.6-27B source
SWE-bench Verified
Coding
Grok 4.20: 76.7%Qwen3.6-27B: 77.2%Normalized gap 0.5Grok 4.20 source Qwen3.6-27B source

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

Grok 4.20: $0.005; 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

Grok 4.20: $0.118; 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

Grok 4.20: $0.5; Fits in one request; Cached input priced at the published list-input rate
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6-27B has no comparable published API token rate.

Specification differences

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

SpecificationGrok 4.20Qwen3.6-27B

Context window

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

Grok 4.20

Qwen3.6-27B

262K

API model ID

Grok 4.20

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.

Grok 4.20

Not published

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

Grok 4.20

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

Grok 4.20

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

Grok 4.20

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

Grok 4.20

Reasoning

Qwen3.6-27B

Reasoning

Weight access

Grok 4.20

Proprietary

Qwen3.6-27B

Open Weight

License

Grok 4.20

Proprietary

Qwen3.6-27B

Open Weight

Release date

Grok 4.20

2026-03-10

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: Grok 4.20 has the higher public score estimate, 67.01 versus 52.73, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Grok 4.20 has the larger documented window (2M).

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.

Grok 4.20

API / mo$6,000

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 evidence53 rows

Agentic

Terminal-Bench 2.0
Grok 4.2047.1%
Source
Qwen3.6-27B59.3%
Source
Qwen3.6-27B leads this result
DeepSearchQA
Grok 4.2062.8%
Source
Qwen3.6-27B—
Not directly comparable
Gert Labs
Shared source
Grok 4.2038.36%
Qwen3.6-27B54.84%
Qwen3.6-27B leads this result
Terminal-Bench 2.1 (Vals)
Grok 4.2044.2%
Source
Qwen3.6-27B—
Not directly comparable
Claw-Eval
Grok 4.20—
Qwen3.6-27B72.4%
Source
Not directly comparable
QwenClawBench
Grok 4.20—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
Grok 4.20—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
Grok 4.20—
Qwen3.6-27B70.3%
Source
Not directly comparable

Coding

LiveCodeBench Pro
Grok 4.2074.2%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Verified
Grok 4.2076.7%
Source
Qwen3.6-27B77.2%
Source
Qwen3.6-27B leads this result
SWE-bench Pro
Grok 4.2051.8%
Source
Qwen3.6-27B53.5%
Source
Qwen3.6-27B leads this result
Vibe Code Bench
Grok 4.204.06%
Source
Qwen3.6-27B—
Not directly comparable
LiveCodeBench (Vals)
Grok 4.2084.3%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench (Vals)
Grok 4.2072.2%
Source
Qwen3.6-27B—
Not directly comparable
SWE Multilingual
Grok 4.20—
Qwen3.6-27B71.3%
Source
Not directly comparable
Terminal-Bench 2.0
Grok 4.20—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench
Grok 4.20—
Qwen3.6-27B83.9%
Source
Not directly comparable
NL2Repo
Grok 4.20—
Qwen3.6-27B36.2%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Grok 4.2053.3%
Source
Qwen3.6-27B—
Not directly comparable
ARC-AGI-3
Grok 4.200.1%
Source
Qwen3.6-27B—
Not directly comparable

Knowledge

GPQA-D
Grok 4.2088.5%
Source
Qwen3.6-27B—
Not directly comparable
HLE w/o tools
Grok 4.2031.6%
Source
Qwen3.6-27B—
Not directly comparable
HealthBench Hard
Grok 4.2020.3%
Source
Qwen3.6-27B—
Not directly comparable
MedXpertQA (Text)
Grok 4.2050.2%
Source
Qwen3.6-27B—
Not directly comparable
GPQA Diamond (Vals)
Grok 4.2088.6%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro (Vals)
Grok 4.2086.3%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro
Grok 4.20—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
Grok 4.20—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
Grok 4.20—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
Grok 4.20—
Qwen3.6-27B91.4%
Source
Not directly comparable
GPQA
Grok 4.20—
Qwen3.6-27B87.8%
Source
Not directly comparable
HLE
Grok 4.20—
Qwen3.6-27B24%
Source
Not directly comparable

Math

HMMT Feb 2025
Grok 4.20—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
Grok 4.20—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
Grok 4.20—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
Grok 4.20—
Qwen3.6-27B80.8%
Source
Not directly comparable
AIME26
Grok 4.20—
Qwen3.6-27B94.1%
Source
Not directly comparable

Multimodal

MMMU-Pro
Grok 4.2075.2%
Source
Qwen3.6-27B75.8%
Source
Qwen3.6-27B leads this result
CharXiv
Grok 4.2060.9%
Source
Qwen3.6-27B78.4%
Source
Qwen3.6-27B leads this result
ERQA
Grok 4.2054.1%
Source
Qwen3.6-27B62.5%
Source
Qwen3.6-27B leads this result
SimpleVQA
Grok 4.2057.4%
Source
Qwen3.6-27B56.1%
Source
Grok 4.20 leads this result
MedXpertQA (MM)
Grok 4.2065.8%
Source
Qwen3.6-27B—
Not directly comparable
MMMU
Grok 4.20—
Qwen3.6-27B82.9%
Source
Not directly comparable
RealWorldQA
Grok 4.20—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
Grok 4.20—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
Grok 4.20—
Qwen3.6-27B81.4%
Source
Not directly comparable
CC-OCR
Grok 4.20—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
Grok 4.20—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
Grok 4.20—
Qwen3.6-27B92.5%
Source
Not directly comparable
Video-MME (with subtitle)
Grok 4.20—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
Grok 4.20—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
Grok 4.20—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
Grok 4.20—
Qwen3.6-27B94.7%
Source
Not directly comparable

Frequently asked questions

Which is better, Grok 4.20 or Qwen3.6-27B?

Grok 4.20 has the higher public score estimate, 67.01 versus 52.73, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Grok 4.20 or Qwen3.6-27B?

Qwen3.6-27B leads the public coding lane, 42.6 to 28.2, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Grok 4.20 or Qwen3.6-27B?

Qwen3.6-27B leads the public agentic tasks lane, 33.9 to 26.5, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Grok 4.20 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, Grok 4.20 or Qwen3.6-27B?

Grok 4.20 has the larger documented context window: 2M, compared with 262K.

Related comparisons

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Last updated September 4, 2026

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Grok 4.20 vs Qwen3.6-27B

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Multimodal

Knowledge

Reasoning

Math

Multilingual

Instruction following

Shape of the matched evidence

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

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, Grok 4.20 or Qwen3.6-27B?

Which is better for coding, Grok 4.20 or Qwen3.6-27B?

Which is better for agentic tasks, Grok 4.20 or Qwen3.6-27B?

Which costs less, Grok 4.20 or Qwen3.6-27B?

Which has the larger context window, Grok 4.20 or Qwen3.6-27B?

Related comparisons

Watch Grok 4.20 vs Qwen3.6-27B