Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Sol
GPT-5.6 Sol leads on the public coding lane, 71.6 to 43.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
GPT-5.6 Sol vs Qwen3.6 Plus
Updated September 24, 2026. Rank says GPT-5.6 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
GPT-5.6 Sol has the higher public score, 78.49 versus 54.59, and the 90% score intervals do not overlap. 11 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 11
- GPT-5.6 Sol only
- 26
- Qwen3.6 Plus only
- 37
- Like-for-like categories
- 3 / 8
Supported: GPT-5.6 Sol 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.
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Sol
GPT-5.6 Sol leads on the public agentic lane, 69.6 to 33.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: strongerLong documents
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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-ratesCache-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-ratesRepository 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.
71.6GPT-5.6 Sol43.3Qwen3.6 Plus
Like-for-like · BenchAlign v5.7
GPT-5.6 Sol leads the like-for-like coding row.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
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.
26 GPT-5.6 Sol only
11 Shared
37 Qwen3.6 Plus only
2 categories rest 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.
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 (Tier 4)Math
Normalized gap 74.7GPT-5.6 Sol:83.000%Qwen3.6 Plus:8.333%FrontierMath v2 (Tiers 1-3)Math
Normalized gap 62.8GPT-5.6 Sol:89.000%Qwen3.6 Plus:26.207%SWE-bench ProCoding
Normalized gap 8.0GPT-5.6 Sol:64.6%Qwen3.6 Plus:56.6%MMMU-ProMultimodal
Normalized gap 4.2GPT-5.6 Sol:83%Qwen3.6 Plus:78.8%GPQAKnowledge
Normalized gap 4.2GPT-5.6 Sol:94.6%Qwen3.6 Plus:90.4%
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.
Agentic
Like-for-like- GPT-5.6 Sol
- 69.6
- Supported · #7/105
- Qwen3.6 Plus
- 33.2
- Supported · #60/105
- Basis
- BenchAlign v5.7 lane · 9 vs 13 public rows
- Reading
- GPT-5.6 Sol leads
Coding
Like-for-like- GPT-5.6 Sol
- 71.6
- Supported · #6/135
- Qwen3.6 Plus
- 43.3
- Supported · #51/135
- Basis
- BenchAlign v5.7 lane · 12 vs 7 public rows
- Reading
- GPT-5.6 Sol leads
Knowledge
Like-for-like- GPT-5.6 Sol
- 78.8
- Supported · #7/158
- Qwen3.6 Plus
- 52.1
- Supported · #53/158
- Basis
- BenchAlign v5.7 lane · 8 vs 8 public rows
- Reading
- GPT-5.6 Sol leads
Multimodal
Directional only- GPT-5.6 Sol
- 87.6
- #5/50
- Qwen3.6 Plus
- 66.2
- #24/50
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.6 Sol
- 87.7
- #28/124
- Qwen3.6 Plus
- 82.5
- #50/124
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.6 Sol
- 72.1
- #8/19
- Qwen3.6 Plus
- 61.2
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Sol
- Not ranked
- Qwen3.6 Plus
- 69.7
- #4/12
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Sol
- 96.8
- Unranked · 3 rankable rows
- Qwen3.6 Plus
- 62.0
- #5/7
- Basis
- Provisional lane · 2 vs 4 weighted rows
- Reading
- Not comparable
| Category | GPT-5.6 Sol | Qwen3.6 Plus | Basis | Reading |
|---|---|---|---|---|
| Agentic | 69.6Supported · #7/105 | 33.2Supported · #60/105 | Like-for-likeBenchAlign v5.7 lane · 9 vs 13 public rows | GPT-5.6 Sol leads |
| Coding | 71.6Supported · #6/135 | 43.3Supported · #51/135 | Like-for-likeBenchAlign v5.7 lane · 12 vs 7 public rows | GPT-5.6 Sol leads |
| Knowledge | 78.8Supported · #7/158 | 52.1Supported · #53/158 | Like-for-likeBenchAlign v5.7 lane · 8 vs 8 public rows | GPT-5.6 Sol leads |
| Multimodal | 87.6#5/50 | 66.2#24/50 | Directional onlyProvisional lane · 1 vs 2 weighted rows | Directional only |
| Instruction following | 87.7#28/124 | 82.5#50/124 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 72.1#8/19 | 61.2Unranked · 4 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | 69.7#4/12 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 96.8Unranked · 3 rankable rows | 62.0#5/7 | Not comparableProvisional lane · 2 vs 4 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-5.6 Sol
- $0.014
- 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-5.6 Sol
- $0.26
- 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-5.6 Sol
- $0.36
- Fits in one request
- Qwen3.6 Plus
- API rate not published
- Fits in one request
- Cached-input rate unavailable
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-5.6 SolQwen3.6 Plus
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Sol
1.05M
OpenAI model catalogQwen3.6 Plus
1M
API model ID
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogQwen3.6 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-5.6 Sol
$0.4 per 1M cached input tokens
OpenAI pricingQwen3.6 Plus
No comparable hosted API rate
Documented inputs
GPT-5.6 Sol
text, image
OpenAI model catalogQwen3.6 Plus
Not sourced
Documented outputs
GPT-5.6 Sol
Qwen3.6 Plus
Not sourced
Provider availability
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogQwen3.6 Plus
Not sourced
Reasoning profile
GPT-5.6 Sol
Reasoning
Qwen3.6 Plus
Reasoning
Weight access
GPT-5.6 Sol
Proprietary
Qwen3.6 Plus
Proprietary
License
GPT-5.6 Sol
Proprietary
Qwen3.6 Plus
Proprietary
Release date
GPT-5.6 Sol
2026-07-09
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
- GPT-5.6 Sol has the higher public score, 78.49 versus 54.59, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.6 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-5.6 Sol or Qwen3.6 Plus?
GPT-5.6 Sol has the higher public score, 78.49 versus 54.59, 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, GPT-5.6 Sol or Qwen3.6 Plus?
GPT-5.6 Sol leads the public coding lane, 71.6 to 43.3, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GPT-5.6 Sol or Qwen3.6 Plus?
GPT-5.6 Sol leads the public agentic tasks lane, 69.6 to 33.2, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, GPT-5.6 Sol 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-5.6 Sol or Qwen3.6 Plus?
GPT-5.6 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 evidence74 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Sol34.6%SourceQwen3.6 Plus—Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Sol91.9%SourceQwen3.6 Plus—Not directly comparable
BrowseComp
GPT-5.6 Sol92.2%SourceQwen3.6 Plus—Not directly comparable
OSWorld 2.0
GPT-5.6 Sol62.6%SourceQwen3.6 Plus—Not directly comparable
CyberGym
GPT-5.6 Sol84.5%SourceQwen3.6 Plus—Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%SourceQwen3.6 Plus—Not directly comparable
Toolathlon
GPT-5.6 Sol58%SourceQwen3.6 Plus39.8%SourceGPT-5.6 Sol leads this result
Terminal-Bench 2.1 (Vals)
GPT-5.6 Sol85.8%SourceQwen3.6 Plus53.2%SourceGPT-5.6 Sol leads this result
ApprenticeBench
GPT-5.6 Sol26%SourceQwen3.6 Plus—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Sol—Qwen3.6 Plus61.6%SourceNot directly comparable
Claw-Eval
GPT-5.6 Sol—Qwen3.6 Plus58.8%SourceNot directly comparable
QwenClawBench
GPT-5.6 Sol—Qwen3.6 Plus57.2%SourceNot directly comparable
τ³-bench results
GPT-5.6 Sol—Qwen3.6 Plus70.7%SourceNot directly comparable
VITA-Bench
GPT-5.6 Sol—Qwen3.6 Plus44.3%SourceNot directly comparable
DeepPlanning
GPT-5.6 Sol—Qwen3.6 Plus41.5%SourceNot directly comparable
MCP Atlas
GPT-5.6 Sol—Qwen3.6 Plus48.2%SourceNot directly comparable
MCP-Tasks
GPT-5.6 Sol—Qwen3.6 Plus74.1%SourceNot directly comparable
WideResearch
GPT-5.6 Sol—Qwen3.6 Plus74.3%SourceNot directly comparable
Gert Labs
GPT-5.6 Sol—Qwen3.6 Plus50.60%SourceNot directly comparable
ResearchClawBench
GPT-5.6 Sol—Qwen3.6 Plus18.0%SourceNot directly comparable
Coding
Bug Hunt Bench
GPT-5.6 Sol42 fixesSourceQwen3.6 Plus—Not directly comparable
SWE-bench Pro
GPT-5.6 Sol64.6%SourceQwen3.6 Plus56.6%SourceGPT-5.6 Sol leads this result
Terminal-Bench 2.1
GPT-5.6 Sol91.9%SourceQwen3.6 Plus—Not directly comparable
DeepSWE
GPT-5.6 Sol72.7%SourceQwen3.6 Plus—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%SourceQwen3.6 Plus—Not directly comparable
FrontierSWE v2
GPT-5.6 Sol32.2%SourceQwen3.6 Plus—Not directly comparable
cursorBench32
GPT-5.6 Sol67.2%SourceQwen3.6 Plus—Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%SourceQwen3.6 Plus—Not directly comparable
VulcanBench CII v1
GPT-5.6 Sol86.5%SourceQwen3.6 Plus—Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Sol82.6%SourceQwen3.6 Plus86.0%SourceQwen3.6 Plus leads this result
SWE-bench (Vals)
GPT-5.6 Sol96.2%SourceQwen3.6 Plus73.4%SourceGPT-5.6 Sol leads this result
cursorBench40
GPT-5.6 Sol41.7%SourceQwen3.6 Plus—Not directly comparable
SWE-bench Verified
GPT-5.6 Sol—Qwen3.6 Plus78.8%SourceNot directly comparable
SWE Multilingual
GPT-5.6 Sol—Qwen3.6 Plus73.8%SourceNot directly comparable
LiveCodeBench v6
GPT-5.6 Sol—Qwen3.6 Plus87.1%SourceNot directly comparable
Vibe Code Bench
GPT-5.6 Sol—Qwen3.6 Plus25.56%SourceNot directly comparable
Reasoning
ARC-AGI-2
GPT-5.6 Sol92.5%SourceQwen3.6 Plus—Not directly comparable
ARC-AGI-3
GPT-5.6 Sol7.8%SourceQwen3.6 Plus—Not directly comparable
GeneBench-Pro
GPT-5.6 Sol28.7%SourceQwen3.6 Plus—Not directly comparable
AI-Needle
GPT-5.6 Sol—Qwen3.6 Plus68.3%SourceNot directly comparable
LongBench v2
GPT-5.6 Sol—Qwen3.6 Plus62%SourceNot directly comparable
Multimodal
MMMU-Pro
GPT-5.6 Sol83%SourceQwen3.6 Plus78.8%SourceGPT-5.6 Sol leads this result
MMMU-Pro w/ Python
GPT-5.6 Sol84.6%SourceQwen3.6 Plus—Not directly comparable
MMMU
GPT-5.6 Sol—Qwen3.6 Plus86.0%SourceNot directly comparable
MathVision
GPT-5.6 Sol—Qwen3.6 Plus88.0%SourceNot directly comparable
VideoMMMU
GPT-5.6 Sol—Qwen3.6 Plus84.0%SourceNot directly comparable
ScreenSpot Pro
GPT-5.6 Sol—Qwen3.6 Plus68.2%SourceNot directly comparable
CharXiv
GPT-5.6 Sol—Qwen3.6 Plus81.5%SourceNot directly comparable
V*
GPT-5.6 Sol—Qwen3.6 Plus96.9%SourceNot directly comparable
Knowledge
GPQA
GPT-5.6 Sol94.6%SourceQwen3.6 Plus90.4%SourceGPT-5.6 Sol leads this result
GPQA-D
GPT-5.6 Sol94.6%SourceQwen3.6 Plus—Not directly comparable
HLE-Verified
GPT-5.6 Sol54.5%SourceQwen3.6 Plus—Not directly comparable
LABBench2
GPT-5.6 Sol82.1%SourceQwen3.6 Plus—Not directly comparable
HealthBench Professional
GPT-5.6 Sol60.5%SourceQwen3.6 Plus—Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%SourceQwen3.6 Plus—Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Sol95.2%SourceQwen3.6 Plus87.4%SourceGPT-5.6 Sol leads this result
MMLU-Pro (Vals)
GPT-5.6 Sol89.1%SourceQwen3.6 Plus87.7%SourceGPT-5.6 Sol leads this result
SuperGPQA
GPT-5.6 Sol—Qwen3.6 Plus71.6%SourceNot directly comparable
MMLU-Pro
GPT-5.6 Sol—Qwen3.6 Plus88.5%SourceNot directly comparable
MMLU-Redux
GPT-5.6 Sol—Qwen3.6 Plus94.5%SourceNot directly comparable
C-Eval
GPT-5.6 Sol—Qwen3.6 Plus93.3%SourceNot directly comparable
HLE
GPT-5.6 Sol—Qwen3.6 Plus28.8%SourceNot directly comparable
Multilingual
Instruction following
Math
FrontierMath (legacy)
GPT-5.6 Sol89%SourceQwen3.6 Plus—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Sol89.000%SourceQwen3.6 Plus26.207%SourceGPT-5.6 Sol leads this result
FrontierMath v2 (Tier 4)
GPT-5.6 Sol83.000%SourceQwen3.6 Plus8.333%SourceGPT-5.6 Sol leads this result
AIME26
GPT-5.6 Sol—Qwen3.6 Plus95.3%SourceNot directly comparable
HMMT Feb 2025
GPT-5.6 Sol—Qwen3.6 Plus96.7%SourceNot directly comparable
HMMT Nov 2025
GPT-5.6 Sol—Qwen3.6 Plus94.6%SourceNot directly comparable
HMMT Feb 2026
GPT-5.6 Sol—Qwen3.6 Plus87.8%SourceNot directly comparable
MMAnswerBench
GPT-5.6 Sol—Qwen3.6 Plus83.8%SourceNot directly comparable
74 public results · 11 shared
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Last updated September 24, 2026