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BenchLM

Grok 4.6 vs Qwen3.5-122B-A10B

Updated September 25, 2026. Rank says Grok 4.6 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
xAI logo
Grok 4.6

xAI

69.19/100

Supported · Public rank #15

90% interval 66.3–72.1

Model B
Alibaba logo
Qwen3.5-122B-A10B

Alibaba

40.06/100

Estimated · Public rank #113

90% interval 19.5–60.6

Shared results
0
Grok 4.6 only
17
Qwen3.5-122B-A10B only
15
Like-for-like categories
2 / 8
Supported: Grok 4.6 · Estimated: Qwen3.5-122B-A10BHow 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

    Grok 4.6

    Grok 4.6 leads on the public coding lane, 62 to 35.7, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • 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

  • Agentic work

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

    Not enough matched evidence

    Qwen3.5-122B-A10B 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.

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

62.0Grok 4.635.7Qwen3.5-122B-A10B

Like-for-like · BenchAlign v5.7

Grok 4.6 leads the like-for-like coding row.

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.

1 category rests 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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.

Coding

Like-for-like
Grok 4.6
62.0
Supported · #14/135
Qwen3.5-122B-A10B
35.7
Supported · #73/135
Basis
BenchAlign v5.7 lane · 8 vs 1 public rows
Reading
Grok 4.6 leads

Knowledge

Like-for-like
Grok 4.6
69.0
Supported · #13/158
Qwen3.5-122B-A10B
41.5
Supported · #86/158
Basis
BenchAlign v5.7 lane · 2 vs 3 public rows
Reading
Grok 4.6 leads

Agentic

Directional only
Grok 4.6
67.9
Supported · #8/105
Qwen3.5-122B-A10B
23.1
Estimated · #84/105
Basis
BenchAlign v5.7 lane · 4 vs 3 public rows
Reading
Directional only

Reasoning

Not comparable
Grok 4.6
57.6
#16/19
Qwen3.5-122B-A10B
49.8
Unranked · 3 rankable rows
Basis
Provisional lane · 2 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Grok 4.6
Not ranked
Qwen3.5-122B-A10B
57.0
#34/50
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Grok 4.6
Not ranked
Qwen3.5-122B-A10B
36.8
#10/12
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Grok 4.6
Not ranked
Qwen3.5-122B-A10B
91.6
#11/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Grok 4.6
Not ranked
Qwen3.5-122B-A10B
Not ranked
Basis
Provisional lane · 0 vs 0 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

Grok 4.6
$0.005
Fits in one request
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request

Qwen3.5-122B-A10B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Grok 4.6
$0.118
Fits in one request
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request

Qwen3.5-122B-A10B 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
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Qwen3.5-122B-A10B 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.

Grok 4.6

500K

xAI Grok 4.6 release notes

Qwen3.5-122B-A10B

262K

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

Qwen3.5-122B-A10B

No comparable hosted API rate

Documented inputs

Grok 4.6

Not sourced

Qwen3.5-122B-A10B

Not sourced

Documented outputs

Grok 4.6

Not sourced

Qwen3.5-122B-A10B

Not sourced

Provider availability

Grok 4.6

Not sourced

Qwen3.5-122B-A10B

Not sourced

Reasoning profile

Grok 4.6

Reasoning

Qwen3.5-122B-A10B

Reasoning

Weight access

Grok 4.6

Proprietary

Qwen3.5-122B-A10B

Open Weight

License

Grok 4.6

Proprietary

Qwen3.5-122B-A10B

Open Weight

Release date

Grok 4.6

2026-08-12

Qwen3.5-122B-A10B

2026-03-04

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
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 Qwen3.5-122B-A10B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Grok 4.6 or Qwen3.5-122B-A10B?

Grok 4.6 leads the public coding lane, 62 to 35.7, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Grok 4.6 or Qwen3.5-122B-A10B?

Grok 4.6 scores higher for agentic tasks on the public lane, 67.9 to 23.1. Qwen3.5-122B-A10B 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 Qwen3.5-122B-A10B?

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 Qwen3.5-122B-A10B?

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

Benchmark evidence

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

Browse raw public benchmark evidence32 rows

Agentic

  • Terminal-Bench 3.0

    Grok 4.626.5%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • APEX-Agents

    Grok 4.657.5%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Grok 4.678.3%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • ApprenticeBench

    Grok 4.613%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • Terminal-Bench 2.0

    Grok 4.6—
    Qwen3.5-122B-A10B49.4%
    Source

    Not directly comparable

  • BrowseComp

    Grok 4.6—
    Qwen3.5-122B-A10B63.8%
    Source

    Not directly comparable

  • OSWorld-Verified

    Grok 4.6—
    Qwen3.5-122B-A10B58%
    Source

    Not directly comparable

Coding

  • Bug Hunt Bench

    Grok 4.627 fixes
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • DeepSWE

    Grok 4.665.9%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • cursorBench32

    Grok 4.670.8%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • FrontierCode 1.1 Extended

    Grok 4.661.3%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • VulcanBench v3

    Grok 4.687.0%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • FrontierSWE v2

    Grok 4.625.3%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • LiveCodeBench (Vals)

    Grok 4.688.2%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • SWE-bench (Vals)

    Grok 4.695.6%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • SWE-bench Verified

    Grok 4.6—
    Qwen3.5-122B-A10B72%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-1

    Grok 4.687.00%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • ARC-AGI-2

    Grok 4.667.1%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • ARC-AGI-3

    Grok 4.62.1%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • LongBench v2

    Grok 4.6—
    Qwen3.5-122B-A10B60.2%
    Source

    Not directly comparable

Multimodal

  • MMMU

    Grok 4.6—
    Qwen3.5-122B-A10B83.9%
    Source

    Not directly comparable

  • MMVU

    Grok 4.6—
    Qwen3.5-122B-A10B74.7%
    Source

    Not directly comparable

  • MathVision

    Grok 4.6—
    Qwen3.5-122B-A10B86.2%
    Source

    Not directly comparable

  • CharXiv

    Grok 4.6—
    Qwen3.5-122B-A10B77.2%
    Source

    Not directly comparable

  • V*

    Grok 4.6—
    Qwen3.5-122B-A10B93.2%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Grok 4.694.7%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • MMLU-Pro (Vals)

    Grok 4.689.4%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • MMLU-Pro

    Grok 4.6—
    Qwen3.5-122B-A10B86.7%
    Source

    Not directly comparable

  • SuperGPQA

    Grok 4.6—
    Qwen3.5-122B-A10B67.1%
    Source

    Not directly comparable

  • GPQA

    Grok 4.6—
    Qwen3.5-122B-A10B86.6%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Grok 4.6—
    Qwen3.5-122B-A10B82.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Grok 4.6—
    Qwen3.5-122B-A10B93.4%
    Source

    Not directly comparable

32 public results · 0 shared

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Last updated September 25, 2026