Grok 4.6 vs Qwen3.6-35B-A3B

Updated November 10, 2026. Rank says Grok 4.6 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Grok 4.6 has the higher public point estimate, 66.47 versus 42.87. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Grok 4.6

xAI

66.47/100

Supported · Public rank #24

90% interval 63.2–69.7

Model B

Qwen3.6-35B-A3B

Alibaba

42.87/100

Estimated · Public rank #128

Conditional range 28.5–57.2

API price per 1M tokens

Grok 4.6: $2 input / $6 output

Qwen3.6-35B-A3B: No comparable API price

Context window

Grok 4.6: 500K

Qwen3.6-35B-A3B: 262K

Released

Grok 4.6: August 12, 2026

Qwen3.6-35B-A3B: April 15, 2026

Shared results: 1
Grok 4.6 only: 23
Qwen3.6-35B-A3B only: 43
Like-for-like categories: 0 / 8

Supported: Grok 4.6 · Estimated: Qwen3.6-35B-A3B. 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.

Long documents
Prompts that approach the documented context limit
Grok 4.6
Grok 4.6 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
Qwen3.6-35B-A3B 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
Qwen3.6-35B-A3B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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
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.

58.6Grok 4.635.8Qwen3.6-35B-A3B

Directional only · BenchAlign v5.8

Grok 4.6 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

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

What do you need a model for?

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

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.

FrontierMath v2 (Tiers 1-3)Math
Normalized gap 45.6
Grok 4.6:65.965%
Qwen3.6-35B-A3B:20.351%
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, reasoning, multimodal, 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

Directional only

Grok 4.6: 69.7; Supported · #11/132
Qwen3.6-35B-A3B: 34.2; Estimated · #93/132
Basis: BenchAlign v5.8 lane · 7 vs 11 public rows
Reading: Directional only

Coding

Directional only

Grok 4.6: 58.6; Supported · #23/158
Qwen3.6-35B-A3B: 35.8; Estimated · #82/158
Basis: BenchAlign v5.8 lane · 9 vs 6 public rows
Reading: Directional only

Reasoning

Directional only

Grok 4.6: 65.0; Supported · #22/158
Qwen3.6-35B-A3B: 36.9; Estimated · #99/158
Basis: BenchAlign v5.8 lane · 3 vs 0 public rows
Reading: Directional only

Knowledge

Directional only

Grok 4.6: 67.9; Supported · #21/179
Qwen3.6-35B-A3B: 42.7; Estimated · #106/179
Basis: BenchAlign v5.8 lane · 2 vs 5 public rows
Reading: Directional only

Multimodal

Not comparable

Grok 4.6: Not ranked
Qwen3.6-35B-A3B: 38.1; Supported · #64/67
Basis: BenchAlign v5.8 lane · 0 vs 16 public rows
Reading: Not comparable

Multilingual

Not comparable

Grok 4.6: Not ranked
Qwen3.6-35B-A3B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Grok 4.6: Not ranked
Qwen3.6-35B-A3B: 78.3; #65/157
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Grok 4.6: 65.6; Unranked · 2 rankable rows
Qwen3.6-35B-A3B: 48.2; #11/12
Basis: Provisional lane · 2 vs 3 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Grok 4.6 and Qwen3.6-35B-A3B
Category	Grok 4.6	Qwen3.6-35B-A3B	Basis	Reading
Agentic	69.7Supported · #11/132	34.2Estimated · #93/132	Directional onlyBenchAlign v5.8 lane · 7 vs 11 public rows	Directional only
Coding	58.6Supported · #23/158	35.8Estimated · #82/158	Directional onlyBenchAlign v5.8 lane · 9 vs 6 public rows	Directional only
Reasoning	65.0Supported · #22/158	36.9Estimated · #99/158	Directional onlyBenchAlign v5.8 lane · 3 vs 0 public rows	Directional only
Knowledge	67.9Supported · #21/179	42.7Estimated · #106/179	Directional onlyBenchAlign v5.8 lane · 2 vs 5 public rows	Directional only
Multimodal	Not ranked	38.1Supported · #64/67	Not comparableBenchAlign v5.8 lane · 0 vs 16 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	78.3#65/157	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	65.6Unranked · 2 rankable rows	48.2#11/12	Not comparableProvisional lane · 2 vs 3 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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

Grok 4.6: $0.005; Fits in one request
Qwen3.6-35B-A3B: API rate not published; Fits in one request

Qwen3.6-35B-A3B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Grok 4.6: $0.118; Fits in one request
Qwen3.6-35B-A3B: API rate not published; Fits in one request

Qwen3.6-35B-A3B has no comparable published API token rate.

Cache-heavy agent loop

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

Grok 4.6: $0.2; Fits in one request
Qwen3.6-35B-A3B: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.6-35B-A3B 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.

SpecificationGrok 4.6Qwen3.6-35B-A3B

Context window

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

Grok 4.6

500K

xAI Grok 4.6 release notes

Qwen3.6-35B-A3B

262K

API model ID

Grok 4.6

grok-4.6

xAI Grok 4.6 release notes

Qwen3.6-35B-A3B

Not sourced

Cached-input rate

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

Grok 4.6

$0.5 per 1M cached input tokens

xAI Grok 4.6 release notes

Qwen3.6-35B-A3B

No comparable hosted API rate

Documented inputs

Grok 4.6

text, image

xAI model page for grok-4.6

Qwen3.6-35B-A3B

text, image, video

Qwen3.6-35B-A3B model card

Documented outputs

Grok 4.6

text

xAI model page for grok-4.6

Qwen3.6-35B-A3B

text

Qwen3.6-35B-A3B model card

Provider availability

Grok 4.6

Not sourced

Qwen3.6-35B-A3B

Not sourced

Reasoning profile

Grok 4.6

Reasoning

Qwen3.6-35B-A3B

Reasoning

Weight access

Grok 4.6

Proprietary

Qwen3.6-35B-A3B

Open Weight

License

Grok 4.6

Proprietary

Qwen3.6-35B-A3B

Open Weight

Release date

Grok 4.6

2026-08-12

Qwen3.6-35B-A3B

2026-04-15

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: Grok 4.6 has the higher public point estimate, 66.47 versus 42.87. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Grok 4.6 has the larger documented window (500K).

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

Questions

Which is better, Grok 4.6 or Qwen3.6-35B-A3B?

Grok 4.6 has the higher public point estimate, 66.47 versus 42.87. Their conditional score ranges do not 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, Grok 4.6 or Qwen3.6-35B-A3B?

Grok 4.6 scores higher for coding on the public lane, 58.6 to 35.8. Qwen3.6-35B-A3B 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, Grok 4.6 or Qwen3.6-35B-A3B?

Grok 4.6 scores higher for agentic tasks on the public lane, 69.7 to 34.2. Qwen3.6-35B-A3B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Grok 4.6 or Qwen3.6-35B-A3B?

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 4.6 or Qwen3.6-35B-A3B?

Grok 4.6 has the larger documented context window: 500K, compared with 262K.

Self-host vs API cost

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

Grok 4.6

API / mo$6,000

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Qwen3.6-35B-A3B

API / mo (DeepInfra)$788

Self-host / mo$298

Break-even19M/day

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 evidence67 rows

Agentic

Terminal-Bench 4.1
Grok 4.619.70%
Source
Qwen3.6-35B-A3B—
Not directly comparable
Terminal-Bench 3.0
Grok 4.626.5%
Source
Qwen3.6-35B-A3B—
Not directly comparable
APEX-Agents
Grok 4.657.5%
Source
Qwen3.6-35B-A3B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Grok 4.678.3%
Source
Qwen3.6-35B-A3B—
Not directly comparable
ApprenticeBench
Grok 4.613%
Source
Qwen3.6-35B-A3B—
Not directly comparable
CWE-bench v1
Grok 4.657.0%
Source
Qwen3.6-35B-A3B—
Not directly comparable
MazeBench
Grok 4.61%
Source
Qwen3.6-35B-A3B—
Not directly comparable
Terminal-Bench 2.0
Grok 4.6—
Qwen3.6-35B-A3B51.5%
Source
Not directly comparable
Claw-Eval
Grok 4.6—
Qwen3.6-35B-A3B68.7%
Source
Not directly comparable
QwenClawBench
Grok 4.6—
Qwen3.6-35B-A3B52.6%
Source
Not directly comparable
QwenWebBench
Grok 4.6—
Qwen3.6-35B-A3B1397
Source
Not directly comparable
τ³-bench results
Grok 4.6—
Qwen3.6-35B-A3B67.2%
Source
Not directly comparable
VITA-Bench
Grok 4.6—
Qwen3.6-35B-A3B35.6%
Source
Not directly comparable
DeepPlanning
Grok 4.6—
Qwen3.6-35B-A3B25.9%
Source
Not directly comparable
Toolathlon
Grok 4.6—
Qwen3.6-35B-A3B26.9%
Source
Not directly comparable
MCP Atlas
Grok 4.6—
Qwen3.6-35B-A3B62.8%
Source
Not directly comparable
WideResearch
Grok 4.6—
Qwen3.6-35B-A3B60.1%
Source
Not directly comparable
Gert Labs
Grok 4.6—
Qwen3.6-35B-A3B42.65%
Source
Not directly comparable

Coding

Bug Hunt Bench
Grok 4.627.0 fixes
Source
Qwen3.6-35B-A3B—
Not directly comparable
DeepSWE
Grok 4.665.9%
Source
Qwen3.6-35B-A3B—
Not directly comparable
CursorBench 3.2
Grok 4.670.8%
Source
Qwen3.6-35B-A3B—
Not directly comparable
FrontierCode 1.1 Extended
Grok 4.661.3%
Source
Qwen3.6-35B-A3B—
Not directly comparable
VulcanBench v3
Grok 4.687.0%
Source
Qwen3.6-35B-A3B—
Not directly comparable
FrontierSWE v2
Grok 4.625.3%
Source
Qwen3.6-35B-A3B—
Not directly comparable
LiveCodeBench (Vals)
Grok 4.688.2%
Source
Qwen3.6-35B-A3B—
Not directly comparable
SWE-bench (Vals)
Grok 4.695.6%
Source
Qwen3.6-35B-A3B—
Not directly comparable
CursorBench 4.0
Grok 4.641.4%
Source
Qwen3.6-35B-A3B—
Not directly comparable
SWE-bench Verified
Grok 4.6—
Qwen3.6-35B-A3B73.4%
Source
Not directly comparable
SWE Multilingual
Grok 4.6—
Qwen3.6-35B-A3B67.2%
Source
Not directly comparable
SWE-bench Pro
Grok 4.6—
Qwen3.6-35B-A3B49.5%
Source
Not directly comparable
Terminal-Bench 2.0
Grok 4.6—
Qwen3.6-35B-A3B51.5%
Source
Not directly comparable
LiveCodeBench v6
Grok 4.6—
Qwen3.6-35B-A3B80.4%
Source
Not directly comparable
NL2Repo
Grok 4.6—
Qwen3.6-35B-A3B29.4%
Source
Not directly comparable

Reasoning

ARC-AGI-1
Grok 4.687.00%
Source
Qwen3.6-35B-A3B—
Not directly comparable
ARC-AGI-2
Grok 4.667.1%
Source
Qwen3.6-35B-A3B—
Not directly comparable
ARC-AGI-3
Grok 4.62.1%
Source
Qwen3.6-35B-A3B—
Not directly comparable

Multimodal

ExtractBench (LlamaIndex)
Grok 4.6—
Qwen3.6-35B-A3B88.11%
Source
Not directly comparable
ParseBench
Grok 4.6—
Qwen3.6-35B-A3B37.35%
Source
Not directly comparable
MMMU
Grok 4.6—
Qwen3.6-35B-A3B81.7%
Source
Not directly comparable
MMMU-Pro
Grok 4.6—
Qwen3.6-35B-A3B75.3%
Source
Not directly comparable
RealWorldQA
Grok 4.6—
Qwen3.6-35B-A3B85.3%
Source
Not directly comparable
OmniDocBench 1.5
Grok 4.6—
Qwen3.6-35B-A3B89.9%
Source
Not directly comparable
CharXiv
Grok 4.6—
Qwen3.6-35B-A3B78%
Source
Not directly comparable
SimpleVQA
Grok 4.6—
Qwen3.6-35B-A3B58.9%
Source
Not directly comparable
CC-OCR
Grok 4.6—
Qwen3.6-35B-A3B81.9%
Source
Not directly comparable
AI2D_TEST
Grok 4.6—
Qwen3.6-35B-A3B92.7%
Source
Not directly comparable
RefCOCO (avg)
Grok 4.6—
Qwen3.6-35B-A3B92.0%
Source
Not directly comparable
ODINW13
Grok 4.6—
Qwen3.6-35B-A3B50.8%
Source
Not directly comparable
Video-MME (with subtitle)
Grok 4.6—
Qwen3.6-35B-A3B86.6%
Source
Not directly comparable
Video-MME (w/o subtitle)
Grok 4.6—
Qwen3.6-35B-A3B82.5%
Source
Not directly comparable
VideoMMMU
Grok 4.6—
Qwen3.6-35B-A3B83.7%
Source
Not directly comparable
MLVU (M-Avg)
Grok 4.6—
Qwen3.6-35B-A3B86.2%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
Grok 4.694.7%
Source
Qwen3.6-35B-A3B—
Not directly comparable
MMLU-Pro (Vals)
Grok 4.689.4%
Source
Qwen3.6-35B-A3B—
Not directly comparable
MMLU-Pro
Grok 4.6—
Qwen3.6-35B-A3B85.2%
Source
Not directly comparable
SuperGPQA
Grok 4.6—
Qwen3.6-35B-A3B64.7%
Source
Not directly comparable
C-Eval
Grok 4.6—
Qwen3.6-35B-A3B90%
Source
Not directly comparable
GPQA
Grok 4.6—
Qwen3.6-35B-A3B86%
Source
Not directly comparable
HLE
Grok 4.6—
Qwen3.6-35B-A3B21.4%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Grok 4.651.8%
Source
Qwen3.6-35B-A3B—
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Shared source
Grok 4.665.965%
Qwen3.6-35B-A3B20.351%
Grok 4.6 leads this result
FrontierMath v2 (Tier 4)
Grok 4.631.707%
Source
Qwen3.6-35B-A3B—
Not directly comparable
HMMT Feb 2025
Grok 4.6—
Qwen3.6-35B-A3B90.7%
Source
Not directly comparable
HMMT Nov 2025
Grok 4.6—
Qwen3.6-35B-A3B89.1%
Source
Not directly comparable
HMMT Feb 2026
Grok 4.6—
Qwen3.6-35B-A3B83.6%
Source
Not directly comparable
MMAnswerBench
Grok 4.6—
Qwen3.6-35B-A3B78.9%
Source
Not directly comparable
AIME26
Grok 4.6—
Qwen3.6-35B-A3B92.7%
Source
Not directly comparable

67 public results · 1 shared

Related comparisons

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Watch Grok 4.6 vs Qwen3.6-35B-A3B

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Last updated November 10, 2026

Grok 4.6 vs Qwen3.6-35B-A3B

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

Or describe your own job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Multimodal

Multilingual

Instruction following

Math

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, Grok 4.6 or Qwen3.6-35B-A3B?

Which is better for coding, Grok 4.6 or Qwen3.6-35B-A3B?

Which is better for agentic tasks, Grok 4.6 or Qwen3.6-35B-A3B?

Which costs less, Grok 4.6 or Qwen3.6-35B-A3B?

Which has the larger context window, Grok 4.6 or Qwen3.6-35B-A3B?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Grok 4.6 vs Qwen3.6-35B-A3B