Grok 4 vs Qwen3.5 397B

Updated September 29, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 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

xAI

52.6/100

Supported · Public rank #70

90% interval 43.3–61.9

Model B

Qwen3.5 397B

Alibaba

—

Evidence status unavailable

90% interval unavailable

Shared results: 1
Grok 4 only: 3
Qwen3.5 397B only: 37
Like-for-like categories: 0 / 8

Supported: Grok 4How 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Grok 4 and Qwen3.5 397B are not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Grok 4 and Qwen3.5 397B are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Grok 4 does not fit this workload in one request. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate. Grok 4 has no comparable published API token rate.
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

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.

—Grok 4—Qwen3.5 397B

Not comparable · BenchAlign v5.7

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

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.

Instruction following

Directional only

Grok 4: 62.9; #71/124
Qwen3.5 397B: 0.0; #124/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Agentic

Not comparable

Grok 4: Not ranked
Qwen3.5 397B: Not ranked
Basis: BenchAlign v5.7 lane · 1 vs 13 public rows
Reading: Not comparable

Coding

Not comparable

Grok 4: Not ranked
Qwen3.5 397B: Not ranked
Basis: BenchAlign v5.7 lane · 1 vs 3 public rows
Reading: Not comparable

Reasoning

Not comparable

Grok 4: 69.0; Unranked · 2 rankable rows
Qwen3.5 397B: 61.0; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Grok 4: 62.5; Unranked · 1 rankable row
Qwen3.5 397B: 63.9; #28/50
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Grok 4: 48.5; Estimated · #65/169
Qwen3.5 397B: Not ranked
Basis: BenchAlign v5.7 lane · 0 vs 6 public rows
Reading: Not comparable

Multilingual

Not comparable

Grok 4: Not ranked
Qwen3.5 397B: 69.7; #5/12
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Grok 4: 38.3; Unranked · 2 rankable rows
Qwen3.5 397B: 73.5; Unranked · 5 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Grok 4 and Qwen3.5 397B
Category	Grok 4	Qwen3.5 397B	Basis	Reading
Instruction following	62.9#71/124	0.0#124/124	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Agentic	Not ranked	Not ranked	Not comparableBenchAlign v5.7 lane · 1 vs 13 public rows	Not comparable
Coding	Not ranked	Not ranked	Not comparableBenchAlign v5.7 lane · 1 vs 3 public rows	Not comparable
Reasoning	69.0Unranked · 2 rankable rows	61.0Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	62.5Unranked · 1 rankable row	63.9#28/50	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Knowledge	48.5Estimated · #65/169	Not ranked	Not comparableBenchAlign v5.7 lane · 0 vs 6 public rows	Not comparable
Multilingual	Not ranked	69.7#5/12	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	38.3Unranked · 2 rankable rows	73.5Unranked · 5 rankable rows	Not comparableProvisional lane · 2 vs 2 weighted rows	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.

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: API rate not published; Fits in one request
Qwen3.5 397B: $0.0024; Fits in one request

Grok 4 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Grok 4: API rate not published; Fits in one request
Qwen3.5 397B: $0.0408; Fits in one request

Grok 4 has no comparable published API token rate.

Cache-heavy agent loop

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

Grok 4: API rate not published; Does not fit in one request; Cached-input rate unavailable
Qwen3.5 397B: $0.168; Does not fit in one request; Cached input priced at the published list-input rate

Grok 4 does not fit this workload in one request. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate. Grok 4 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 4Qwen3.5 397B

Context window

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

Grok 4

128K

Qwen3.5 397B

128K

API model ID

Grok 4

Not sourced

Qwen3.5 397B

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

No comparable hosted API rate

Qwen3.5 397B

Not published

Documented inputs

Grok 4

Not sourced

Qwen3.5 397B

Not sourced

Documented outputs

Grok 4

Not sourced

Qwen3.5 397B

Not sourced

Provider availability

Grok 4

Not sourced

Qwen3.5 397B

Not sourced

Reasoning profile

Grok 4

Non-Reasoning

Qwen3.5 397B

Non-Reasoning

Weight access

Grok 4

Proprietary

Qwen3.5 397B

Open Weight

License

Grok 4

Proprietary

Qwen3.5 397B

Open Weight

Release date

Grok 4

2025-07-09

Qwen3.5 397B

2026-02-16

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: At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Both models list 128K.

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

Questions

Which is better, Grok 4 or Qwen3.5 397B?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Grok 4 or Qwen3.5 397B?

Grok 4 and Qwen3.5 397B are not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Grok 4 or Qwen3.5 397B?

Grok 4 and Qwen3.5 397B are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Grok 4 or Qwen3.5 397B?

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 or Qwen3.5 397B?

Both models list the same context window, 128K.

Benchmark evidence

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

Browse raw public benchmark evidence41 rows

Agentic

Gert Labs
Shared source
Grok 442.34%
Qwen3.5 397B46.76%
Qwen3.5 397B leads this result
Terminal-Bench 2.0
Grok 4—
Qwen3.5 397B52.5%
Source
Not directly comparable
BrowseComp
Grok 4—
Qwen3.5 397B62%
Source
Not directly comparable
Claw-Eval
Grok 4—
Qwen3.5 397B56.8%
Source
Not directly comparable
QwenClawBench
Grok 4—
Qwen3.5 397B51.8%
Source
Not directly comparable
τ³-bench results
Grok 4—
Qwen3.5 397B68.4%
Source
Not directly comparable
VITA-Bench
Grok 4—
Qwen3.5 397B43.7%
Source
Not directly comparable
DeepPlanning
Grok 4—
Qwen3.5 397B37.6%
Source
Not directly comparable
Toolathlon
Grok 4—
Qwen3.5 397B36.3%
Source
Not directly comparable
MCP Atlas
Grok 4—
Qwen3.5 397B46.1%
Source
Not directly comparable
MCP-Tasks
Grok 4—
Qwen3.5 397B74.2%
Source
Not directly comparable
WideResearch
Grok 4—
Qwen3.5 397B74.0%
Source
Not directly comparable
ResearchClawBench
Grok 4—
Qwen3.5 397B14.2%
Source
Not directly comparable

Coding

React Native Evals
Grok 472.6%
Source
Qwen3.5 397B—
Not directly comparable
SWE-bench Verified
Grok 4—
Qwen3.5 397B76.2%
Source
Not directly comparable
LiveCodeBench v6
Grok 4—
Qwen3.5 397B83.6%
Source
Not directly comparable
SWE-bench Pro
Grok 4—
Qwen3.5 397B50.9%
Source
Not directly comparable

Reasoning

LongBench v2
Grok 4—
Qwen3.5 397B63.2%
Source
Not directly comparable
AI-Needle
Grok 4—
Qwen3.5 397B68.7%
Source
Not directly comparable

Multimodal

MMMU-Pro
Grok 4—
Qwen3.5 397B79%
Source
Not directly comparable
MathVision
Grok 4—
Qwen3.5 397B88.6%
Source
Not directly comparable
CharXiv
Grok 4—
Qwen3.5 397B80.8%
Source
Not directly comparable
VideoMMMU
Grok 4—
Qwen3.5 397B84.7%
Source
Not directly comparable
ScreenSpot Pro
Grok 4—
Qwen3.5 397B65.6%
Source
Not directly comparable
V*
Grok 4—
Qwen3.5 397B95.8%
Source
Not directly comparable

Knowledge

GPQA
Grok 4—
Qwen3.5 397B88.4%
Source
Not directly comparable
SuperGPQA
Grok 4—
Qwen3.5 397B70.4%
Source
Not directly comparable
MMLU-Pro
Grok 4—
Qwen3.5 397B87.8%
Source
Not directly comparable
MMLU-Redux
Grok 4—
Qwen3.5 397B94.9%
Source
Not directly comparable
C-Eval
Grok 4—
Qwen3.5 397B93%
Source
Not directly comparable
HLE
Grok 4—
Qwen3.5 397B28.7%
Source
Not directly comparable

Multilingual

MMLU-ProX
Grok 4—
Qwen3.5 397B84.7%
Source
Not directly comparable
NOVA-63
Grok 4—
Qwen3.5 397B59.1%
Source
Not directly comparable

Instruction following

IFEval
Grok 4—
Qwen3.5 397B92.6%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Grok 419.655%
Source
Qwen3.5 397B—
Not directly comparable
FrontierMath v2 (Tier 4)
Grok 42.083%
Source
Qwen3.5 397B—
Not directly comparable
AIME26
Grok 4—
Qwen3.5 397B93.3%
Source
Not directly comparable
HMMT Feb 2025
Grok 4—
Qwen3.5 397B94.8%
Source
Not directly comparable
HMMT Nov 2025
Grok 4—
Qwen3.5 397B92.7%
Source
Not directly comparable
HMMT Feb 2026
Grok 4—
Qwen3.5 397B87.9%
Source
Not directly comparable
MMAnswerBench
Grok 4—
Qwen3.5 397B80.9%
Source
Not directly comparable

41 public results · 1 shared

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Last updated September 29, 2026

Grok 4 vs Qwen3.5 397B

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

Instruction following

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

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 or Qwen3.5 397B?

Which is better for coding, Grok 4 or Qwen3.5 397B?

Which is better for agentic tasks, Grok 4 or Qwen3.5 397B?

Which costs less, Grok 4 or Qwen3.5 397B?

Which has the larger context window, Grok 4 or Qwen3.5 397B?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Grok 4 vs Qwen3.5 397B