GPT-6.1 Sol vs Qwen3.8 Max

Updated September 29, 2026. Rank says Qwen3.8 Max is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.8 Max has the higher public score estimate, 71.44 versus 66.86, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 2 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-6.1 Sol

OpenAI

66.86/100

Estimated · Public rank #23

90% interval 55.4–78.4

Model B

Qwen3.8 Max

Alibaba

71.44/100

Supported · Public rank #14

90% interval 68.4–74.5

Shared results: 2
GPT-6.1 Sol only: 7
Qwen3.8 Max only: 58
Like-for-like categories: 1 / 8

Estimated: GPT-6.1 Sol · Supported: Qwen3.8 MaxHow 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
GPT-6.1 Sol
GPT-6.1 Sol leads on the public coding lane, 67 to 55.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-6.1 Sol
GPT-6.1 Sol 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
GPT-6.1 Sol is not ranked on the public lane for agentic, so no winner is named for agentic.
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.

67.0GPT-6.1 Sol55.6Qwen3.8 Max

Like-for-like · BenchAlign v5.7

GPT-6.1 Sol leads the like-for-like coding row, although the 90% intervals overlap.

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.

AutomationBenchAgentic
Normalized gap 8.8
GPT-6.1 Sol:36.1%
Qwen3.8 Max:27.3%
GPT-6.1 Sol source Qwen3.8 Max 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 v5.7 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

GPT-6.1 Sol: 67.0; Supported · #8/143
Qwen3.8 Max: 55.6; Supported · #28/143
Basis: BenchAlign v5.7 lane · 1 vs 12 public rows
Reading: GPT-6.1 Sol leads · intervals overlap

Knowledge

Directional only

GPT-6.1 Sol: 71.3; Estimated · #10/169
Qwen3.8 Max: 66.2; Supported · #22/169
Basis: BenchAlign v5.7 lane · 5 vs 6 public rows
Reading: Directional only

Agentic

Not comparable

GPT-6.1 Sol: Not ranked
Qwen3.8 Max: 65.1; Supported · #12/117
Basis: BenchAlign v5.7 lane · 3 vs 15 public rows
Reading: Not comparable

Reasoning

Not comparable

GPT-6.1 Sol: 79.4; Unranked · 2 rankable rows
Qwen3.8 Max: 87.7; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-6.1 Sol: 83.9; Unranked · 1 rankable row
Qwen3.8 Max: 88.4; #5/50
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-6.1 Sol: Not ranked
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-6.1 Sol: Not ranked
Qwen3.8 Max: 90.5; #16/124
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-6.1 Sol: Not ranked
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-6.1 Sol and Qwen3.8 Max
Category	GPT-6.1 Sol	Qwen3.8 Max	Basis	Reading
Coding	67.0Supported · #8/143	55.6Supported · #28/143	Like-for-likeBenchAlign v5.7 lane · 1 vs 12 public rows	GPT-6.1 Sol leads · intervals overlap
Knowledge	71.3Estimated · #10/169	66.2Supported · #22/169	Directional onlyBenchAlign v5.7 lane · 5 vs 6 public rows	Directional only
Agentic	Not ranked	65.1Supported · #12/117	Not comparableBenchAlign v5.7 lane · 3 vs 15 public rows	Not comparable
Reasoning	79.4Unranked · 2 rankable rows	87.7Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multimodal	83.9Unranked · 1 rankable row	88.4#5/50	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	90.5#16/124	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 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.

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-6.1 Sol: $0.007; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Qwen3.8 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-6.1 Sol: $0.13; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Qwen3.8 Max has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-6.1 Sol: $0.16; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.8 Max 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-6.1 SolQwen3.8 Max

Context window

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

GPT-6.1 Sol

1.05M

OpenAI GPT-6.1 Sol model documentation

Qwen3.8 Max

Alibaba Cloud Model Studio pricing

API model ID

GPT-6.1 Sol

gpt-6.1-sol

OpenAI GPT-6.1 Sol model documentation

Qwen3.8 Max

qwen3.8-max

Alibaba Cloud Model Studio pricing

Cached-input rate

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

GPT-6.1 Sol

$0.1 per 1M cached input tokens

OpenAI GPT-6.1 Sol model documentation

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

GPT-6.1 Sol

text, image

OpenAI GPT-6.1 Sol model documentation

Qwen3.8 Max

Not sourced

Documented outputs

GPT-6.1 Sol

text

OpenAI GPT-6.1 Sol model documentation

Qwen3.8 Max

Not sourced

Provider availability

GPT-6.1 Sol

Generally Available · OpenAI Responses API, OpenAI Chat Completions API

OpenAI GPT-6.1 Sol model documentation

Qwen3.8 Max

Not sourced

Reasoning profile

GPT-6.1 Sol

Reasoning

Qwen3.8 Max

Reasoning

Weight access

GPT-6.1 Sol

Proprietary

Qwen3.8 Max

Open Weight

License

GPT-6.1 Sol

Proprietary

Qwen3.8 Max

Open Weight

Release date

GPT-6.1 Sol

2026-09-29

Qwen3.8 Max

2026-08-03

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: Qwen3.8 Max has the higher public score estimate, 71.44 versus 66.86, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-6.1 Sol has the larger documented window (1.05M).

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

Questions

Which is better, GPT-6.1 Sol or Qwen3.8 Max?

Qwen3.8 Max has the higher public score estimate, 71.44 versus 66.86, 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-6.1 Sol or Qwen3.8 Max?

GPT-6.1 Sol leads the public coding lane, 67 to 55.6, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GPT-6.1 Sol or Qwen3.8 Max?

GPT-6.1 Sol is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-6.1 Sol or Qwen3.8 Max?

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-6.1 Sol or Qwen3.8 Max?

GPT-6.1 Sol has the larger documented context window: 1.05M, compared with 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 evidence67 rows

Agentic

AutomationBench
GPT-6.1 Sol36.1%
Source
Qwen3.8 Max27.3%
Source
GPT-6.1 Sol leads this result
Terminal-Bench-Science 0.1
GPT-6.1 Sol57.0%
Source
Qwen3.8 Max—
Not directly comparable
ExploitGym
GPT-6.1 Sol35.1%
Source
Qwen3.8 Max—
Not directly comparable
Terminal-Bench 2.1
GPT-6.1 Sol—
Qwen3.8 Max86.6%
Source
Not directly comparable
CoWorkBench
GPT-6.1 Sol—
Qwen3.8 Max74.8%
Source
Not directly comparable
JobBench
GPT-6.1 Sol—
Qwen3.8 Max53.4%
Source
Not directly comparable
skillsBench
GPT-6.1 Sol—
Qwen3.8 Max70.2%
Source
Not directly comparable
Agents' Last Exam
GPT-6.1 Sol—
Qwen3.8 Max52.4%
Source
Not directly comparable
Toolathlon-Verified
GPT-6.1 Sol—
Qwen3.8 Max72.5%
Source
Not directly comparable
WideResearch
GPT-6.1 Sol—
Qwen3.8 Max81.9%
Source
Not directly comparable
HLE w/ tools
GPT-6.1 Sol—
Qwen3.8 Max56.2%
Source
Not directly comparable
OSWorld-Verified
GPT-6.1 Sol—
Qwen3.8 Max86.1%
Source
Not directly comparable
OSWorld 2.0
GPT-6.1 Sol—
Qwen3.8 Max19.4%
Source
Not directly comparable
WebArena-Verified
GPT-6.1 Sol—
Qwen3.8 Max66.8%
Source
Not directly comparable
AndroidWorld
GPT-6.1 Sol—
Qwen3.8 Max85.3%
Source
Not directly comparable
MobileWorld
GPT-6.1 Sol—
Qwen3.8 Max77.8%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-6.1 Sol—
Qwen3.8 Max67.4%
Source
Not directly comparable

Coding

DeepSWE
GPT-6.1 Sol71.9%
Source
Qwen3.8 Max56.6%
Source
GPT-6.1 Sol leads this result
Terminal-Bench 2.1
GPT-6.1 Sol—
Qwen3.8 Max86.6%
Source
Not directly comparable
SWE-bench Pro
GPT-6.1 Sol—
Qwen3.8 Max67.7%
Source
Not directly comparable
NL2Repo
GPT-6.1 Sol—
Qwen3.8 Max55.9%
Source
Not directly comparable
FrontierSWE
GPT-6.1 Sol—
Qwen3.8 Max73.5%
Source
Not directly comparable
MLS-Bench Lite
GPT-6.1 Sol—
Qwen3.8 Max41.0%
Source
Not directly comparable
PaperBench
GPT-6.1 Sol—
Qwen3.8 Max93.0%
Source
Not directly comparable
VulcanBench v3
GPT-6.1 Sol—
Qwen3.8 Max81.2%
Source
Not directly comparable
OpenHarmony Bench
GPT-6.1 Sol—
Qwen3.8 Max60.8%
Source
Not directly comparable
FrontierSWE v2
GPT-6.1 Sol—
Qwen3.8 Max15.8%
Source
Not directly comparable
LiveCodeBench (Vals)
GPT-6.1 Sol—
Qwen3.8 Max87.9%
Source
Not directly comparable
SWE-bench (Vals)
GPT-6.1 Sol—
Qwen3.8 Max85.6%
Source
Not directly comparable

Reasoning

MRCRv2
GPT-6.1 Sol—
Qwen3.8 Max92.9%
Source
Not directly comparable
LongBench v2
GPT-6.1 Sol—
Qwen3.8 Max66.3%
Source
Not directly comparable

Multimodal

MMMU-Pro
GPT-6.1 Sol—
Qwen3.8 Max82.3%
Source
Not directly comparable
MathVision
GPT-6.1 Sol—
Qwen3.8 Max95.2%
Source
Not directly comparable
MathVision w/ Python
GPT-6.1 Sol—
Qwen3.8 Max97.7%
Source
Not directly comparable
BabyVision
GPT-6.1 Sol—
Qwen3.8 Max82.0%
Source
Not directly comparable
BabyVision w/ Python
GPT-6.1 Sol—
Qwen3.8 Max91.3%
Source
Not directly comparable
ZeroBench
GPT-6.1 Sol—
Qwen3.8 Max24.0%
Source
Not directly comparable
ZeroBench w/ Python
GPT-6.1 Sol—
Qwen3.8 Max49.0%
Source
Not directly comparable
MedXpertQA (MM)
GPT-6.1 Sol—
Qwen3.8 Max80.4%
Source
Not directly comparable
ScreenSpot Pro
GPT-6.1 Sol—
Qwen3.8 Max84.5%
Source
Not directly comparable
Vision2Web
GPT-6.1 Sol—
Qwen3.8 Max69.0%
Source
Not directly comparable
CharXiv w/o tools
GPT-6.1 Sol—
Qwen3.8 Max88.4%
Source
Not directly comparable
CharXiv
GPT-6.1 Sol—
Qwen3.8 Max93.5%
Source
Not directly comparable
OmniDocBench 1.5
GPT-6.1 Sol—
Qwen3.8 Max92.1%
Source
Not directly comparable
OCRBench V2
GPT-6.1 Sol—
Qwen3.8 Max74.2%
Source
Not directly comparable
CC-OCR
GPT-6.1 Sol—
Qwen3.8 Max79.6%
Source
Not directly comparable
RealWorldQA
GPT-6.1 Sol—
Qwen3.8 Max88.0%
Source
Not directly comparable
ERQA
GPT-6.1 Sol—
Qwen3.8 Max77.8%
Source
Not directly comparable
SimpleVQA
GPT-6.1 Sol—
Qwen3.8 Max75.0%
Source
Not directly comparable
PerceptionBench
GPT-6.1 Sol—
Qwen3.8 Max63.5%
Source
Not directly comparable
Video-MME (with subtitle)
GPT-6.1 Sol—
Qwen3.8 Max90.4%
Source
Not directly comparable
VideoMMMU
GPT-6.1 Sol—
Qwen3.8 Max88.7%
Source
Not directly comparable
MMVU
GPT-6.1 Sol—
Qwen3.8 Max82.4%
Source
Not directly comparable
MLVU (M-Avg)
GPT-6.1 Sol—
Qwen3.8 Max90.8%
Source
Not directly comparable
LVBench
GPT-6.1 Sol—
Qwen3.8 Max81.8%
Source
Not directly comparable

Knowledge

HealthBench (raw)
GPT-6.1 Sol56.7%
Source
Qwen3.8 Max—
Not directly comparable
HealthBench (length-adjusted)
GPT-6.1 Sol58.5%
Source
Qwen3.8 Max—
Not directly comparable
HealthBench Professional
GPT-6.1 Sol64.2%
Source
Qwen3.8 Max—
Not directly comparable
HealthBench Professional (raw)
GPT-6.1 Sol67.2%
Source
Qwen3.8 Max—
Not directly comparable
HealthBench Hard
GPT-6.1 Sol36.2%
Source
Qwen3.8 Max—
Not directly comparable
GPQA
GPT-6.1 Sol—
Qwen3.8 Max92.6%
Source
Not directly comparable
GPQA-D
GPT-6.1 Sol—
Qwen3.8 Max92.6%
Source
Not directly comparable
HLE
GPT-6.1 Sol—
Qwen3.8 Max43.6%
Source
Not directly comparable
HLE w/o tools
GPT-6.1 Sol—
Qwen3.8 Max43.6%
Source
Not directly comparable
GPQA Diamond (Vals)
GPT-6.1 Sol—
Qwen3.8 Max93.7%
Source
Not directly comparable
MMLU-Pro (Vals)
GPT-6.1 Sol—
Qwen3.8 Max88.6%
Source
Not directly comparable

Instruction following

IFBench
GPT-6.1 Sol—
Qwen3.8 Max82.8%
Source
Not directly comparable

67 public results · 2 shared

Related comparisons

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Last updated September 29, 2026

GPT-6.1 Sol vs Qwen3.8 Max

Decision reading

Which one for your work

Coding work

Long documents

Agentic work

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

Coding

Knowledge

Agentic

Reasoning

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, GPT-6.1 Sol or Qwen3.8 Max?

Which is better for coding, GPT-6.1 Sol or Qwen3.8 Max?

Which is better for agentic tasks, GPT-6.1 Sol or Qwen3.8 Max?

Which costs less, GPT-6.1 Sol or Qwen3.8 Max?

Which has the larger context window, GPT-6.1 Sol or Qwen3.8 Max?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Related comparisons

Watch GPT-6.1 Sol vs Qwen3.8 Max