GPT-5.6 Sol vs Ling 3.0 Flash

Updated October 20, 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.

Decision reading

GPT-5.6 Sol has the higher public point estimate, 75.32 versus 45.07. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 9 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-5.6 Sol

OpenAI

75.32/100

Supported · Public rank #10

90% interval 72.6–78.0

Model B

Ling 3.0 Flash

InclusionAI

45.07/100

Estimated · Public rank #121

Conditional range 30.7–59.4

Shared results: 9
GPT-5.6 Sol only: 34
Ling 3.0 Flash only: 15
Like-for-like categories: 3 / 8

Supported: GPT-5.6 Sol · Estimated: Ling 3.0 Flash. Conditional ranges do not establish rank confidence.How 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-5.6 Sol
GPT-5.6 Sol has the higher public coding point estimate, 67.4 to 31.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Sol
GPT-5.6 Sol has the higher public agentic point estimate, 68.6 to 33.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long 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-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.

67.4GPT-5.6 Sol31.6Ling 3.0 Flash

Like-for-like · BenchAlign v5.8

GPT-5.6 Sol has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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.

BrowseCompAgentic
Normalized gap 20.0
GPT-5.6 Sol:92.2%
Ling 3.0 Flash:72.2%
GPT-5.6 Sol source Ling 3.0 Flash source
GPQAKnowledge
Normalized gap 9.6
GPT-5.6 Sol:94.6%
Ling 3.0 Flash:85.0%
GPT-5.6 Sol source Ling 3.0 Flash source
SWE-bench ProCoding
Normalized gap 8.0
GPT-5.6 Sol:64.6%
Ling 3.0 Flash:56.6%
GPT-5.6 Sol source Ling 3.0 Flash source
MMLU-Pro (Vals)Knowledge
Normalized gap 7.1
GPT-5.6 Sol:89.1%
Ling 3.0 Flash:82.0%
GPT-5.6 Sol source Ling 3.0 Flash source
LiveCodeBench (Vals)Coding
Normalized gap 1.4
GPT-5.6 Sol:82.6%
Ling 3.0 Flash:84.0%
GPT-5.6 Sol source Ling 3.0 Flash 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.

Agentic

Like-for-like

GPT-5.6 Sol: 68.6; Supported · #12/131
Ling 3.0 Flash: 33.4; Supported · #87/131
Basis: BenchAlign v5.8 lane · 10 vs 9 public rows
Reading: GPT-5.6 Sol leads

Coding

Like-for-like

GPT-5.6 Sol: 67.4; Supported · #11/152
Ling 3.0 Flash: 31.6; Supported · #92/152
Basis: BenchAlign v5.8 lane · 13 vs 6 public rows
Reading: GPT-5.6 Sol leads

Knowledge

Like-for-like

GPT-5.6 Sol: 77.3; Supported · #11/187
Ling 3.0 Flash: 43.5; Supported · #107/187
Basis: BenchAlign v5.8 lane · 8 vs 5 public rows
Reading: GPT-5.6 Sol leads

Instruction following

Directional only

GPT-5.6 Sol: 89.1; #31/149
Ling 3.0 Flash: 75.2; #68/149
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.6 Sol: 76.4; #13/27
Ling 3.0 Flash: 75.1; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.6 Sol: 89.8; #6/55
Ling 3.0 Flash: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.6 Sol: Not ranked
Ling 3.0 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.6 Sol: 95.8; Unranked · 3 rankable rows
Ling 3.0 Flash: 81.1; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.6 Sol and Ling 3.0 Flash
Category	GPT-5.6 Sol	Ling 3.0 Flash	Basis	Reading
Agentic	68.6Supported · #12/131	33.4Supported · #87/131	Like-for-likeBenchAlign v5.8 lane · 10 vs 9 public rows	GPT-5.6 Sol leads
Coding	67.4Supported · #11/152	31.6Supported · #92/152	Like-for-likeBenchAlign v5.8 lane · 13 vs 6 public rows	GPT-5.6 Sol leads
Knowledge	77.3Supported · #11/187	43.5Supported · #107/187	Like-for-likeBenchAlign v5.8 lane · 8 vs 5 public rows	GPT-5.6 Sol leads
Instruction following	89.1#31/149	75.2#68/149	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	76.4#13/27	75.1Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multimodal	89.8#6/55	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	95.8Unranked · 3 rankable rows	81.1Unranked · 3 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 v5.8) 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-5.6 Sol: $0.014; Fits in one request
Ling 3.0 Flash: API rate not published; Fits in one request

Ling 3.0 Flash 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
Ling 3.0 Flash: API rate not published; Fits in one request

Ling 3.0 Flash 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
Ling 3.0 Flash: API rate not published; Fits in one request; Cached-input rate unavailable

Ling 3.0 Flash 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 SolLing 3.0 Flash

Context window

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

GPT-5.6 Sol

1.05M

OpenAI model catalog

Ling 3.0 Flash

262K

InclusionAI Ling 3.0 Flash model card

API model ID

GPT-5.6 Sol

gpt-5.6-sol

OpenAI model catalog

Ling 3.0 Flash

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 pricing

Ling 3.0 Flash

No comparable hosted API rate

InclusionAI Ling 3.0 Flash model card

Documented inputs

GPT-5.6 Sol

text, image

OpenAI model catalog

Ling 3.0 Flash

Not sourced

Documented outputs

GPT-5.6 Sol

text

OpenAI model catalog

Ling 3.0 Flash

Not sourced

Provider availability

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

Ling 3.0 Flash

Not sourced

Reasoning profile

GPT-5.6 Sol

Reasoning

Ling 3.0 Flash

Reasoning

Weight access

GPT-5.6 Sol

Proprietary

Ling 3.0 Flash

Open Weight

License

GPT-5.6 Sol

Proprietary

Ling 3.0 Flash

Open Weight

Release date

GPT-5.6 Sol

2026-07-09

Ling 3.0 Flash

2026-07-23

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 point estimate, 75.32 versus 45.07. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
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 Ling 3.0 Flash?

GPT-5.6 Sol has the higher public point estimate, 75.32 versus 45.07. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-5.6 Sol or Ling 3.0 Flash?

GPT-5.6 Sol has the higher public coding point estimate, 67.4 to 31.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, GPT-5.6 Sol or Ling 3.0 Flash?

GPT-5.6 Sol has the higher public agentic tasks point estimate, 68.6 to 33.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, GPT-5.6 Sol or Ling 3.0 Flash?

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 Ling 3.0 Flash?

GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 262K.

Benchmark evidence

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

Browse raw public benchmark evidence58 rows

Agentic

Terminal-Bench 3.0
GPT-5.6 Sol34.6%
Source
Ling 3.0 Flash—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Sol91.9%
Source
Ling 3.0 Flash—
Not directly comparable
BrowseComp
GPT-5.6 Sol92.2%
Source
Ling 3.0 Flash72.2%
Source
GPT-5.6 Sol leads this result
OSWorld 2.0
GPT-5.6 Sol62.6%
Source
Ling 3.0 Flash—
Not directly comparable
CyberGym
GPT-5.6 Sol84.5%
Source
Ling 3.0 Flash—
Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%
Source
Ling 3.0 Flash—
Not directly comparable
Toolathlon
GPT-5.6 Sol58%
Source
Ling 3.0 Flash—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Sol85.8%
Source
Ling 3.0 Flash50.2%
Source
GPT-5.6 Sol leads this result
ApprenticeBench
GPT-5.6 Sol26%
Source
Ling 3.0 Flash—
Not directly comparable
MazeBench
GPT-5.6 Sol13%
Source
Ling 3.0 Flash—
Not directly comparable
MCP Atlas
GPT-5.6 Sol—
Ling 3.0 Flash65.5%
Source
Not directly comparable
skillsBench
GPT-5.6 Sol—
Ling 3.0 Flash44.8%
Source
Not directly comparable
BFCL v4
GPT-5.6 Sol—
Ling 3.0 Flash73.0%
Source
Not directly comparable
WideResearch
GPT-5.6 Sol—
Ling 3.0 Flash73.6%
Source
Not directly comparable
DRACO
GPT-5.6 Sol—
Ling 3.0 Flash70.4%
Source
Not directly comparable
Hermes Index
GPT-5.6 Sol—
Ling 3.0 Flash21.6%
Source
Not directly comparable
Hermes Bench
GPT-5.6 Sol—
Ling 3.0 Flash53.8%
Source
Not directly comparable

Coding

Bug Hunt Bench
GPT-5.6 Sol42.0 fixes
Source
Ling 3.0 Flash—
Not directly comparable
SWE-bench Pro
GPT-5.6 Sol64.6%
Source
Ling 3.0 Flash56.6%
Source
GPT-5.6 Sol leads this result
Terminal-Bench 2.1
GPT-5.6 Sol91.9%
Source
Ling 3.0 Flash—
Not directly comparable
DeepSWE
GPT-5.6 Sol72.7%
Source
Ling 3.0 Flash—
Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%
Source
Ling 3.0 Flash—
Not directly comparable
FrontierSWE v2
GPT-5.6 Sol32.2%
Source
Ling 3.0 Flash—
Not directly comparable
CursorBench 3.2
GPT-5.6 Sol67.2%
Source
Ling 3.0 Flash—
Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%
Source
Ling 3.0 Flash—
Not directly comparable
VulcanBench CII v1
GPT-5.6 Sol86.5%
Source
Ling 3.0 Flash—
Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Sol82.6%
Source
Ling 3.0 Flash84.0%
Source
Ling 3.0 Flash leads this result
SWE-bench (Vals)
GPT-5.6 Sol96.2%
Source
Ling 3.0 Flash65.2%
Source
GPT-5.6 Sol leads this result
CursorBench 4.0
GPT-5.6 Sol41.7%
Source
Ling 3.0 Flash—
Not directly comparable
PostTrainBench v1.1
GPT-5.6 Sol36.2%
Source
Ling 3.0 Flash—
Not directly comparable
SWE Multilingual
GPT-5.6 Sol—
Ling 3.0 Flash72.4%
Source
Not directly comparable
LiveCodeBench v5
GPT-5.6 Sol—
Ling 3.0 Flash82.8%
Source
Not directly comparable
SciCode
GPT-5.6 Sol—
Ling 3.0 Flash41.2%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.6 Sol92.5%
Source
Ling 3.0 Flash—
Not directly comparable
ARC-AGI-3
GPT-5.6 Sol7.8%
Source
Ling 3.0 Flash—
Not directly comparable
GeneBench-Pro
GPT-5.6 Sol28.7%
Source
Ling 3.0 Flash—
Not directly comparable
ARC-AGI-1
GPT-5.6 Sol96.50%
Source
Ling 3.0 Flash—
Not directly comparable

Multimodal

ExtractBench (LlamaIndex)
GPT-5.6 Sol93.77%
Source
Ling 3.0 Flash—
Not directly comparable
ParseBench
GPT-5.6 Sol75.35%
Source
Ling 3.0 Flash—
Not directly comparable
MMMU-Pro
GPT-5.6 Sol83%
Source
Ling 3.0 Flash—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.6 Sol84.6%
Source
Ling 3.0 Flash—
Not directly comparable

Knowledge

GPQA
GPT-5.6 Sol94.6%
Source
Ling 3.0 Flash85.0%
Source
GPT-5.6 Sol leads this result
GPQA-D
GPT-5.6 Sol94.6%
Source
Ling 3.0 Flash85.0%
Source
GPT-5.6 Sol leads this result
HLE-Verified
GPT-5.6 Sol54.5%
Source
Ling 3.0 Flash—
Not directly comparable
LABBench2
GPT-5.6 Sol82.1%
Source
Ling 3.0 Flash—
Not directly comparable
HealthBench Professional
GPT-5.6 Sol60.5%
Source
Ling 3.0 Flash—
Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%
Source
Ling 3.0 Flash—
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Sol95.2%
Source
Ling 3.0 Flash84.8%
Source
GPT-5.6 Sol leads this result
MMLU-Pro (Vals)
GPT-5.6 Sol89.1%
Source
Ling 3.0 Flash82.0%
Source
GPT-5.6 Sol leads this result
HLE
GPT-5.6 Sol—
Ling 3.0 Flash22.7%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
GPT-5.6 Sol27.0%
Source
Ling 3.0 Flash—
Not directly comparable
IFBench
GPT-5.6 Sol—
Ling 3.0 Flash74.5%
Source
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.6 Sol89%
Source
Ling 3.0 Flash—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Sol89.000%
Source
Ling 3.0 Flash—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Sol83.000%
Source
Ling 3.0 Flash—
Not directly comparable
AIME26
GPT-5.6 Sol—
Ling 3.0 Flash93.2%
Source
Not directly comparable
HMMT Feb 2026
GPT-5.6 Sol—
Ling 3.0 Flash87.0%
Source
Not directly comparable
IMOAnswerBench
GPT-5.6 Sol—
Ling 3.0 Flash83.7%
Source
Not directly comparable

58 public results · 9 shared

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Last updated October 20, 2026

GPT-5.6 Sol vs Ling 3.0 Flash

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

Agentic

Coding

Knowledge

Instruction following

Reasoning

Multimodal

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, GPT-5.6 Sol or Ling 3.0 Flash?

Which is better for coding, GPT-5.6 Sol or Ling 3.0 Flash?

Which is better for agentic tasks, GPT-5.6 Sol or Ling 3.0 Flash?

Which costs less, GPT-5.6 Sol or Ling 3.0 Flash?

Which has the larger context window, GPT-5.6 Sol or Ling 3.0 Flash?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch GPT-5.6 Sol vs Ling 3.0 Flash