Model A

xAI

75.2/100

Supported · Public rank #10

90% interval 70.6–79.8

Grok 4.5 vs Qwen3.5 397B

Updated August 22, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.5 397B

Alibaba

57.5/100

Estimated · Public rank #84

90% interval 46.0–69.0

Decision reading

Grok 4.5 has the higher public score, 75.19 versus 57.49, and the 90% score intervals do not overlap.

2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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 4.5
Grok 4.5 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Qwen3.5 397B
Qwen3.5 397B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

Show secondary and unsupported calls

Repository review cost
50K fresh input + 3K output tokens
Qwen3.5 397B
Qwen3.5 397B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
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. 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.
Confidence: rate-fallback

What is actually comparable

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

8 Grok 4.5 only

2 Shared

36 Qwen3.5 397B only

Shared results: 2
Grok 4.5 only: 8
Qwen3.5 397B only: 36
Like-for-like categories: 0 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Directional only

Grok 4.5: 83.3
Qwen3.5 397B: 56.5
Weighted basis: 1 vs 2 rows
Reading: Directional only

Coding

Directional only

Grok 4.5: 64.7
Qwen3.5 397B: 66.5
Weighted basis: 1 vs 2 rows
Reading: Directional only

Reasoning

Not comparable

Grok 4.5: 52.6
Qwen3.5 397B: 63.2
Weighted basis: 1 vs 1 rows
Reading: Not comparable

Knowledge

Not comparable

Grok 4.5: Not measured
Qwen3.5 397B: 56.6
Weighted basis: 0 vs 4 rows
Reading: Not comparable

Math

Not comparable

Grok 4.5: Not measured
Qwen3.5 397B: 90.6
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

Grok 4.5: Not measured
Qwen3.5 397B: 84.7
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Multimodal

Not comparable

Grok 4.5: Not measured
Qwen3.5 397B: 79.6
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Instruction following

Not comparable

Grok 4.5: Not measured
Qwen3.5 397B: 92.6
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Grok 4.5 and Qwen3.5 397B
Category	Grok 4.5	Qwen3.5 397B	Weighted basis	Reading
Agentic	83.3	56.5	Directional only1 vs 2 rows	Directional only
Coding	64.7	66.5	Directional only1 vs 2 rows	Directional only
Reasoning	52.6	63.2	Not comparable1 vs 1 rows	Not comparable
Knowledge	Not measured	56.6	Not comparable0 vs 4 rows	Not comparable
Math	Not measured	90.6	Not comparable0 vs 2 rows	Not comparable
Multilingual	Not measured	84.7	Not comparable0 vs 1 rows	Not comparable
Multimodal	Not measured	79.6	Not comparable0 vs 2 rows	Not comparable
Instruction following	Not measured	92.6	Not comparable0 vs 1 rows	Not comparable

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.

Terminal-Bench 2.0
Agentic
Grok 4.5: 83.3%Qwen3.5 397B: 52.5%Normalized gap 30.8Grok 4.5 source Qwen3.5 397B source
SWE-bench Pro
Coding
Grok 4.5: 64.7%Qwen3.5 397B: 50.9%Normalized gap 13.8Grok 4.5 source Qwen3.5 397B 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 4.5: $0.005; Fits in one request
Qwen3.5 397B: $0.0024; Fits in one request

Qwen3.5 397B has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Grok 4.5: $0.118; Fits in one request
Qwen3.5 397B: $0.0408; Fits in one request

Qwen3.5 397B has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Grok 4.5: $0.16; Fits in one request
Qwen3.5 397B: $0.168; Does not fit in one request; Cached input priced at the published list-input rate

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.

Specification differences

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

SpecificationGrok 4.5Qwen3.5 397B

Context window

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

Grok 4.5

500K

Qwen3.5 397B

128K

API model ID

Grok 4.5

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 4.5

$0.3 per 1M cached input tokens

Qwen3.5 397B

Not published

Documented inputs

Grok 4.5

Not sourced

Qwen3.5 397B

Not sourced

Documented outputs

Grok 4.5

Not sourced

Qwen3.5 397B

Not sourced

Provider availability

Grok 4.5

Not sourced

Qwen3.5 397B

Not sourced

Reasoning profile

Grok 4.5

Reasoning

Qwen3.5 397B

Non-Reasoning

Weight access

Grok 4.5

Proprietary

Qwen3.5 397B

Open Weight

License

Grok 4.5

Proprietary

Qwen3.5 397B

Open Weight

Release date

Grok 4.5

2026-07-08

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: Grok 4.5 has the higher public score, 75.19 versus 57.49, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.118 vs $0.0408. Cache-heavy agent loop: $0.16 vs $0.168.
Context tradeoff: Grok 4.5 has the larger documented window (500K).

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

Benchmark evidence

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

Browse raw public benchmark evidence46 rows

Agentic

Terminal-Bench 3.0
Grok 4.515.7%
Source
Qwen3.5 397B—
Not directly comparable
Terminal-Bench 2.0
Grok 4.583.3%
Source
Qwen3.5 397B52.5%
Source
Grok 4.5 leads this result
deepSwe
Grok 4.553%
Source
Qwen3.5 397B—
Not directly comparable
BrowseComp
Grok 4.5—
Qwen3.5 397B62%
Source
Not directly comparable
Claw-Eval
Grok 4.5—
Qwen3.5 397B56.8%
Source
Not directly comparable
QwenClawBench
Grok 4.5—
Qwen3.5 397B51.8%
Source
Not directly comparable
τ³-bench results
Grok 4.5—
Qwen3.5 397B68.4%
Source
Not directly comparable
VITA-Bench
Grok 4.5—
Qwen3.5 397B43.7%
Source
Not directly comparable
DeepPlanning
Grok 4.5—
Qwen3.5 397B37.6%
Source
Not directly comparable
Toolathlon
Grok 4.5—
Qwen3.5 397B36.3%
Source
Not directly comparable
MCP Atlas
Grok 4.5—
Qwen3.5 397B46.1%
Source
Not directly comparable
MCP-Tasks
Grok 4.5—
Qwen3.5 397B74.2%
Source
Not directly comparable
WideResearch
Grok 4.5—
Qwen3.5 397B74.0%
Source
Not directly comparable
Gert Labs
Grok 4.5—
Qwen3.5 397B46.76%
Source
Not directly comparable
ResearchClawBench
Grok 4.5—
Qwen3.5 397B14.2%
Source
Not directly comparable

Coding

SWE-bench Pro
Grok 4.564.7%
Source
Qwen3.5 397B50.9%
Source
Grok 4.5 leads this result
SWE Multilingual
Grok 4.578%
Source
Qwen3.5 397B—
Not directly comparable
Terminal-Bench 2.0
Grok 4.583.3%
Source
Qwen3.5 397B—
Not directly comparable
cursorBench32
Grok 4.566.7%
Source
Qwen3.5 397B—
Not directly comparable
VulcanBench v3
Grok 4.591.3%
Source
Qwen3.5 397B—
Not directly comparable
SWE-bench Verified
Grok 4.5—
Qwen3.5 397B76.2%
Source
Not directly comparable
LiveCodeBench v6
Grok 4.5—
Qwen3.5 397B83.6%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Grok 4.552.6%
Source
Qwen3.5 397B—
Not directly comparable
ARC-AGI-3
Grok 4.50.3%
Source
Qwen3.5 397B—
Not directly comparable
LongBench v2
Grok 4.5—
Qwen3.5 397B63.2%
Source
Not directly comparable
AI-Needle
Grok 4.5—
Qwen3.5 397B68.7%
Source
Not directly comparable

Knowledge

GPQA
Grok 4.5—
Qwen3.5 397B88.4%
Source
Not directly comparable
SuperGPQA
Grok 4.5—
Qwen3.5 397B70.4%
Source
Not directly comparable
MMLU-Pro
Grok 4.5—
Qwen3.5 397B87.8%
Source
Not directly comparable
MMLU-Redux
Grok 4.5—
Qwen3.5 397B94.9%
Source
Not directly comparable
C-Eval
Grok 4.5—
Qwen3.5 397B93%
Source
Not directly comparable
HLE
Grok 4.5—
Qwen3.5 397B28.7%
Source
Not directly comparable

Math

AIME26
Grok 4.5—
Qwen3.5 397B93.3%
Source
Not directly comparable
HMMT Feb 2025
Grok 4.5—
Qwen3.5 397B94.8%
Source
Not directly comparable
HMMT Nov 2025
Grok 4.5—
Qwen3.5 397B92.7%
Source
Not directly comparable
HMMT Feb 2026
Grok 4.5—
Qwen3.5 397B87.9%
Source
Not directly comparable
MMAnswerBench
Grok 4.5—
Qwen3.5 397B80.9%
Source
Not directly comparable

Multilingual

MMLU-ProX
Grok 4.5—
Qwen3.5 397B84.7%
Source
Not directly comparable
NOVA-63
Grok 4.5—
Qwen3.5 397B59.1%
Source
Not directly comparable

Multimodal

MMMU-Pro
Grok 4.5—
Qwen3.5 397B79%
Source
Not directly comparable
MathVision
Grok 4.5—
Qwen3.5 397B88.6%
Source
Not directly comparable
CharXiv
Grok 4.5—
Qwen3.5 397B80.8%
Source
Not directly comparable
VideoMMMU
Grok 4.5—
Qwen3.5 397B84.7%
Source
Not directly comparable
ScreenSpot Pro
Grok 4.5—
Qwen3.5 397B65.6%
Source
Not directly comparable
V*
Grok 4.5—
Qwen3.5 397B95.8%
Source
Not directly comparable

Instruction following

IFEval
Grok 4.5—
Qwen3.5 397B92.6%
Source
Not directly comparable

Frequently asked questions

Which is better, Grok 4.5 or Qwen3.5 397B?

Grok 4.5 has the higher public score, 75.19 versus 57.49, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Grok 4.5 or Qwen3.5 397B?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Grok 4.5 or Qwen3.5 397B?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Grok 4.5 or Qwen3.5 397B?

For the stated presets, chat costs $0.005 on Grok 4.5 and $0.0024 on Qwen3.5 397B; repository review costs $0.118 and $0.0408; the cache-heavy agent loop costs $0.16 and $0.168. 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.

Which has the larger context window, Grok 4.5 or Qwen3.5 397B?

Grok 4.5 has the larger documented context window: 500K, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 22, 2026

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Grok 4.5 vs Qwen3.5 397B

Which one for your work

Long documents

Chat turn cost

Repository review cost

Coding work

Agentic work

Cache-heavy agent loop cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Frequently asked questions

Which is better, Grok 4.5 or Qwen3.5 397B?

Which is better for coding, Grok 4.5 or Qwen3.5 397B?

Which is better for agentic tasks, Grok 4.5 or Qwen3.5 397B?

Which costs less, Grok 4.5 or Qwen3.5 397B?

Which has the larger context window, Grok 4.5 or Qwen3.5 397B?

Related comparisons

Watch Grok 4.5 vs Qwen3.5 397B