GPT-4.1 vs Qwen3.6 Plus

Updated October 20, 2026. Rank says Qwen3.6 Plus is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.6 Plus has the higher public score estimate, 53.75 versus 37.55, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-4.1

OpenAI

37.55/100

Supported · Public rank #147

90% interval 21.7–53.4

Model B

Qwen3.6 Plus

Alibaba

53.75/100

Supported · Public rank #76

90% interval 44.1–63.4

Shared results: 6
GPT-4.1 only: 1
Qwen3.6 Plus only: 43
Like-for-like categories: 2 / 8

Supported: GPT-4.1 and Qwen3.6 PlusHow 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
GPT-4.1 is 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
GPT-4.1 is 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
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

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.

—GPT-4.140.0Qwen3.6 Plus

Not comparable · BenchAlign v5.8

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.

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 32.3
GPT-4.1:5.965%
Qwen3.6 Plus:38.246%
Shared source
SWE-bench VerifiedCoding
Normalized gap 24.2
GPT-4.1:54.6%
Qwen3.6 Plus:78.8%
GPT-4.1 source Qwen3.6 Plus source
GPQAKnowledge
Normalized gap 24.1
GPT-4.1:66.3%
Qwen3.6 Plus:90.4%
GPT-4.1 source Qwen3.6 Plus source
MMLU-ProXMultilingual
Normalized gap 12.0
GPT-4.1:72.7%
Qwen3.6 Plus:84.7%
GPT-4.1 source Qwen3.6 Plus 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, 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.

Knowledge

Like-for-like

GPT-4.1: 35.6; Supported · #135/187
Qwen3.6 Plus: 53.4; Supported · #68/187
Basis: BenchAlign v5.8 lane · 2 vs 8 public rows
Reading: Qwen3.6 Plus leads · intervals overlap

Multilingual

Like-for-like

GPT-4.1: 61.5; #14/17
Qwen3.6 Plus: 93.8; #4/17
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Qwen3.6 Plus leads

Instruction following

Directional only

GPT-4.1: 50.3; #93/149
Qwen3.6 Plus: 85.8; #47/149
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Agentic

Not comparable

GPT-4.1: Not ranked
Qwen3.6 Plus: 34.4; Supported · #85/131
Basis: BenchAlign v5.8 lane · 1 vs 13 public rows
Reading: Not comparable

Coding

Not comparable

GPT-4.1: Not ranked
Qwen3.6 Plus: 40.0; Supported · #67/152
Basis: BenchAlign v5.8 lane · 1 vs 9 public rows
Reading: Not comparable

Reasoning

Not comparable

GPT-4.1: 71.9; Unranked · 2 rankable rows
Qwen3.6 Plus: 73.9; Unranked · 4 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-4.1: 54.7; Unranked · 1 rankable row
Qwen3.6 Plus: 76.1; #21/55
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-4.1: 27.0; Unranked · 1 rankable row
Qwen3.6 Plus: 69.5; #6/12
Basis: Provisional lane · 1 vs 3 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-4.1 and Qwen3.6 Plus
Category	GPT-4.1	Qwen3.6 Plus	Basis	Reading
Knowledge	35.6Supported · #135/187	53.4Supported · #68/187	Like-for-likeBenchAlign v5.8 lane · 2 vs 8 public rows	Qwen3.6 Plus leads · intervals overlap
Multilingual	61.5#14/17	93.8#4/17	Like-for-likeProvisional lane · 1 vs 1 weighted rows	Qwen3.6 Plus leads
Instruction following	50.3#93/149	85.8#47/149	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Agentic	Not ranked	34.4Supported · #85/131	Not comparableBenchAlign v5.8 lane · 1 vs 13 public rows	Not comparable
Coding	Not ranked	40.0Supported · #67/152	Not comparableBenchAlign v5.8 lane · 1 vs 9 public rows	Not comparable
Reasoning	71.9Unranked · 2 rankable rows	73.9Unranked · 4 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	54.7Unranked · 1 rankable row	76.1#21/55	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Math	27.0Unranked · 1 rankable row	69.5#6/12	Not comparableProvisional lane · 1 vs 3 weighted rows	Not comparable

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

GPT-4.1: $0.006; Fits in one request
Qwen3.6 Plus: API rate not published; Fits in one request

Qwen3.6 Plus has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-4.1: $0.124; Fits in one request
Qwen3.6 Plus: API rate not published; Fits in one request

Qwen3.6 Plus has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-4.1: $0.52; Fits in one request; Cached input priced at the published list-input rate
Qwen3.6 Plus: API rate not published; Fits in one request; Cached-input rate unavailable

GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6 Plus 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.

SpecificationGPT-4.1Qwen3.6 Plus

Context window

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

GPT-4.1

Qwen3.6 Plus

API model ID

GPT-4.1

Not sourced

Qwen3.6 Plus

Not sourced

Cached-input rate

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

GPT-4.1

Not published

Qwen3.6 Plus

No comparable hosted API rate

Documented inputs

GPT-4.1

Not sourced

Qwen3.6 Plus

Not sourced

Documented outputs

GPT-4.1

Not sourced

Qwen3.6 Plus

Not sourced

Provider availability

GPT-4.1

Not sourced

Qwen3.6 Plus

Not sourced

Reasoning profile

GPT-4.1

Non-Reasoning

Qwen3.6 Plus

Reasoning

Weight access

GPT-4.1

Proprietary

Qwen3.6 Plus

Proprietary

License

GPT-4.1

Proprietary

Qwen3.6 Plus

Proprietary

Release date

GPT-4.1

2025-04-14

Qwen3.6 Plus

2026-04-02

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: Qwen3.6 Plus has the higher public score estimate, 53.75 versus 37.55, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Both models list 1M.

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

Questions

Which is better, GPT-4.1 or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public score estimate, 53.75 versus 37.55, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-4.1 or Qwen3.6 Plus?

GPT-4.1 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GPT-4.1 or Qwen3.6 Plus?

GPT-4.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-4.1 or Qwen3.6 Plus?

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, GPT-4.1 or Qwen3.6 Plus?

Both models list the same context window, 1M.

Benchmark evidence

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

Browse raw public benchmark evidence50 rows

Agentic

Gert Labs
Shared source
GPT-4.125.65%
Qwen3.6 Plus50.60%
Qwen3.6 Plus leads this result
Terminal-Bench 2.0
GPT-4.1—
Qwen3.6 Plus61.6%
Source
Not directly comparable
Claw-Eval
GPT-4.1—
Qwen3.6 Plus58.8%
Source
Not directly comparable
QwenClawBench
GPT-4.1—
Qwen3.6 Plus57.2%
Source
Not directly comparable
τ³-bench results
GPT-4.1—
Qwen3.6 Plus70.7%
Source
Not directly comparable
VITA-Bench
GPT-4.1—
Qwen3.6 Plus44.3%
Source
Not directly comparable
DeepPlanning
GPT-4.1—
Qwen3.6 Plus41.5%
Source
Not directly comparable
Toolathlon
GPT-4.1—
Qwen3.6 Plus39.8%
Source
Not directly comparable
MCP Atlas
GPT-4.1—
Qwen3.6 Plus48.2%
Source
Not directly comparable
MCP-Tasks
GPT-4.1—
Qwen3.6 Plus74.1%
Source
Not directly comparable
WideResearch
GPT-4.1—
Qwen3.6 Plus74.3%
Source
Not directly comparable
ResearchClawBench
GPT-4.1—
Qwen3.6 Plus18.0%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-4.1—
Qwen3.6 Plus53.2%
Source
Not directly comparable

Coding

SWE-bench Verified
GPT-4.154.6%
Source
Qwen3.6 Plus78.8%
Source
Qwen3.6 Plus leads this result
SWE-bench Pro
GPT-4.1—
Qwen3.6 Plus56.6%
Source
Not directly comparable
SWE Multilingual
GPT-4.1—
Qwen3.6 Plus73.8%
Source
Not directly comparable
LiveCodeBench v6
GPT-4.1—
Qwen3.6 Plus87.1%
Source
Not directly comparable
NL2Repo
GPT-4.1—
Qwen3.6 Plus37.9%
Source
Not directly comparable
Terminal-Bench 2.0
GPT-4.1—
Qwen3.6 Plus61.6%
Source
Not directly comparable
Vibe Code Bench
GPT-4.1—
Qwen3.6 Plus25.56%
Source
Not directly comparable
LiveCodeBench (Vals)
GPT-4.1—
Qwen3.6 Plus86.0%
Source
Not directly comparable
SWE-bench (Vals)
GPT-4.1—
Qwen3.6 Plus73.4%
Source
Not directly comparable

Reasoning

AI-Needle
GPT-4.1—
Qwen3.6 Plus68.3%
Source
Not directly comparable
LongBench v2
GPT-4.1—
Qwen3.6 Plus62%
Source
Not directly comparable

Multimodal

MMMU
GPT-4.1—
Qwen3.6 Plus86.0%
Source
Not directly comparable
MMMU-Pro
GPT-4.1—
Qwen3.6 Plus78.8%
Source
Not directly comparable
MathVision
GPT-4.1—
Qwen3.6 Plus88.0%
Source
Not directly comparable
VideoMMMU
GPT-4.1—
Qwen3.6 Plus84.0%
Source
Not directly comparable
ScreenSpot Pro
GPT-4.1—
Qwen3.6 Plus68.2%
Source
Not directly comparable
CharXiv
GPT-4.1—
Qwen3.6 Plus81.5%
Source
Not directly comparable
V*
GPT-4.1—
Qwen3.6 Plus96.9%
Source
Not directly comparable

Knowledge

MMLU
GPT-4.190.2%
Source
Qwen3.6 Plus—
Not directly comparable
GPQA
GPT-4.166.3%
Source
Qwen3.6 Plus90.4%
Source
Qwen3.6 Plus leads this result
SuperGPQA
GPT-4.1—
Qwen3.6 Plus71.6%
Source
Not directly comparable
MMLU-Pro
GPT-4.1—
Qwen3.6 Plus88.5%
Source
Not directly comparable
MMLU-Redux
GPT-4.1—
Qwen3.6 Plus94.5%
Source
Not directly comparable
C-Eval
GPT-4.1—
Qwen3.6 Plus93.3%
Source
Not directly comparable
HLE
GPT-4.1—
Qwen3.6 Plus28.8%
Source
Not directly comparable
GPQA Diamond (Vals)
GPT-4.1—
Qwen3.6 Plus87.4%
Source
Not directly comparable
MMLU-Pro (Vals)
GPT-4.1—
Qwen3.6 Plus87.7%
Source
Not directly comparable

Multilingual

MMLU-ProX
GPT-4.172.7%
Source
Qwen3.6 Plus84.7%
Source
Qwen3.6 Plus leads this result
NOVA-63
GPT-4.1—
Qwen3.6 Plus57.9%
Source
Not directly comparable

Instruction following

IFEval
GPT-4.187.4%
Source
Qwen3.6 Plus94.3%
Source
Qwen3.6 Plus leads this result
IFBench
GPT-4.1—
Qwen3.6 Plus75.8%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Shared source
GPT-4.15.965%
Qwen3.6 Plus38.246%
Qwen3.6 Plus leads this result
AIME26
GPT-4.1—
Qwen3.6 Plus95.3%
Source
Not directly comparable
HMMT Feb 2025
GPT-4.1—
Qwen3.6 Plus96.7%
Source
Not directly comparable
HMMT Nov 2025
GPT-4.1—
Qwen3.6 Plus94.6%
Source
Not directly comparable
HMMT Feb 2026
GPT-4.1—
Qwen3.6 Plus87.8%
Source
Not directly comparable
MMAnswerBench
GPT-4.1—
Qwen3.6 Plus83.8%
Source
Not directly comparable

50 public results · 6 shared

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Last updated October 20, 2026

GPT-4.1 vs Qwen3.6 Plus

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

Knowledge

Multilingual

Instruction following

Agentic

Coding

Reasoning

Multimodal

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, GPT-4.1 or Qwen3.6 Plus?

Which is better for coding, GPT-4.1 or Qwen3.6 Plus?

Which is better for agentic tasks, GPT-4.1 or Qwen3.6 Plus?

Which costs less, GPT-4.1 or Qwen3.6 Plus?

Which has the larger context window, GPT-4.1 or Qwen3.6 Plus?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch GPT-4.1 vs Qwen3.6 Plus