Gemini 3.5 Flash vs GPT-5.6 Sol

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.

Decision reading

GPT-5.6 Sol has the higher public score, 78.49 versus 63.49, and the 90% score intervals do not overlap. 15 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemini 3.5 Flash

Google

63.49/100

Supported · Public rank #32

90% interval 55.9–71.1

Model B

GPT-5.6 Sol

OpenAI

78.49/100

Supported · Public rank #7

90% interval 75.6–81.4

Shared results: 15
Gemini 3.5 Flash only: 12
GPT-5.6 Sol only: 22
Like-for-like categories: 3 / 8

Supported: Gemini 3.5 Flash and GPT-5.6 SolHow 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 leads on the public coding lane, 71.6 to 53.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
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 50, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
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
Gemini 3.5 Flash
Gemini 3.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 3.5 Flash
Gemini 3.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Gemini 3.5 Flash
Gemini 3.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.

53.2Gemini 3.5 Flash71.6GPT-5.6 Sol

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.

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 (Tier 4)Math
Normalized gap 68.4
Gemini 3.5 Flash:14.583%
GPT-5.6 Sol:83.000%
Gemini 3.5 Flash source GPT-5.6 Sol source
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 50.0
Gemini 3.5 Flash:38.966%
GPT-5.6 Sol:89.000%
Gemini 3.5 Flash source GPT-5.6 Sol source
ARC-AGI-2Reasoning
Normalized gap 20.4
Gemini 3.5 Flash:72.1%
GPT-5.6 Sol:92.5%
Gemini 3.5 Flash source GPT-5.6 Sol source
cursorBench32Coding
Normalized gap 18.4
Gemini 3.5 Flash:48.8%
GPT-5.6 Sol:67.2%
Shared source
SWE-bench ProCoding
Normalized gap 9.5
Gemini 3.5 Flash:55.1%
GPT-5.6 Sol:64.6%
Gemini 3.5 Flash source GPT-5.6 Sol 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.

Agentic

Like-for-like

Gemini 3.5 Flash: 50.0; Supported · #35/105
GPT-5.6 Sol: 69.6; Supported · #7/105
Basis: BenchAlign v5.7 lane · 8 vs 9 public rows
Reading: GPT-5.6 Sol leads

Coding

Like-for-like

Gemini 3.5 Flash: 53.2; Supported · #33/135
GPT-5.6 Sol: 71.6; Supported · #6/135
Basis: BenchAlign v5.7 lane · 7 vs 12 public rows
Reading: GPT-5.6 Sol leads

Knowledge

Like-for-like

Gemini 3.5 Flash: 64.4; Supported · #24/158
GPT-5.6 Sol: 78.8; Supported · #7/158
Basis: BenchAlign v5.7 lane · 4 vs 8 public rows
Reading: GPT-5.6 Sol leads · intervals overlap

Reasoning

Directional only

Gemini 3.5 Flash: 62.7; #13/19
GPT-5.6 Sol: 72.1; #8/19
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Directional only

Multimodal

Directional only

Gemini 3.5 Flash: 86.8; #7/50
GPT-5.6 Sol: 87.6; #5/50
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Directional only

Instruction following

Directional only

Gemini 3.5 Flash: 84.1; #43/124
GPT-5.6 Sol: 87.7; #28/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Multilingual

Not comparable

Gemini 3.5 Flash: Not ranked
GPT-5.6 Sol: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.5 Flash: 55.1; Unranked · 2 rankable rows
GPT-5.6 Sol: 96.8; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 3.5 Flash and GPT-5.6 Sol
Category	Gemini 3.5 Flash	GPT-5.6 Sol	Basis	Reading
Agentic	50.0Supported · #35/105	69.6Supported · #7/105	Like-for-likeBenchAlign v5.7 lane · 8 vs 9 public rows	GPT-5.6 Sol leads
Coding	53.2Supported · #33/135	71.6Supported · #6/135	Like-for-likeBenchAlign v5.7 lane · 7 vs 12 public rows	GPT-5.6 Sol leads
Knowledge	64.4Supported · #24/158	78.8Supported · #7/158	Like-for-likeBenchAlign v5.7 lane · 4 vs 8 public rows	GPT-5.6 Sol leads · intervals overlap
Reasoning	62.7#13/19	72.1#8/19	Directional onlyProvisional lane · 2 vs 2 weighted rows	Directional only
Multimodal	86.8#7/50	87.6#5/50	Directional onlyProvisional lane · 2 vs 1 weighted rows	Directional only
Instruction following	84.1#43/124	87.7#28/124	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	55.1Unranked · 2 rankable rows	96.8Unranked · 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 v5.7) 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

Gemini 3.5 Flash: $0.006; Fits in one request
GPT-5.6 Sol: $0.014; Fits in one request

Gemini 3.5 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.5 Flash: $0.102; Fits in one request
GPT-5.6 Sol: $0.26; Fits in one request

Gemini 3.5 Flash has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Gemini 3.5 Flash: $0.15; Fits in one request
GPT-5.6 Sol: $0.36; Fits in one request

Gemini 3.5 Flash has the lower modeled cost

Costs use the listed standard API rates.

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.

SpecificationGemini 3.5 FlashGPT-5.6 Sol

Context window

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

Gemini 3.5 Flash

Google Gemini API pricing

GPT-5.6 Sol

1.05M

OpenAI model catalog

API model ID

Gemini 3.5 Flash

gemini-3.5-flash

Google Gemini API pricing

GPT-5.6 Sol

gpt-5.6-sol

OpenAI model catalog

Cached-input rate

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

Gemini 3.5 Flash

$0.15 per 1M cached input tokens

Google Gemini API pricing

GPT-5.6 Sol

$0.4 per 1M cached input tokens

OpenAI pricing

Documented inputs

Gemini 3.5 Flash

Not sourced

GPT-5.6 Sol

text, image

OpenAI model catalog

Documented outputs

Gemini 3.5 Flash

Not sourced

GPT-5.6 Sol

text

OpenAI model catalog

Provider availability

Gemini 3.5 Flash

Not sourced

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Gemini 3.5 Flash

Reasoning

GPT-5.6 Sol

Reasoning

Weight access

Gemini 3.5 Flash

Proprietary

GPT-5.6 Sol

Proprietary

License

Gemini 3.5 Flash

Proprietary

GPT-5.6 Sol

Proprietary

Release date

Gemini 3.5 Flash

2026-05-19

GPT-5.6 Sol

2026-07-09

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 63.49, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.102 vs $0.26. Cache-heavy agent loop: $0.15 vs $0.36.
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, Gemini 3.5 Flash or GPT-5.6 Sol?

GPT-5.6 Sol has the higher public score, 78.49 versus 63.49, 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, Gemini 3.5 Flash or GPT-5.6 Sol?

GPT-5.6 Sol leads the public coding lane, 71.6 to 53.2, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Gemini 3.5 Flash or GPT-5.6 Sol?

GPT-5.6 Sol leads the public agentic tasks lane, 69.6 to 50, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, Gemini 3.5 Flash or GPT-5.6 Sol?

For the stated presets, chat costs $0.006 on Gemini 3.5 Flash and $0.014 on GPT-5.6 Sol; repository review costs $0.102 and $0.26; the cache-heavy agent loop costs $0.15 and $0.36. Costs use the listed standard API rates.

Which has the larger context window, Gemini 3.5 Flash or GPT-5.6 Sol?

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 evidence49 rows

Agentic

Terminal-Bench 2.1
Gemini 3.5 Flash76.2%
Source
GPT-5.6 Sol91.9%
Source
GPT-5.6 Sol leads this result
MCP Atlas
Gemini 3.5 Flash83.6%
Source
GPT-5.6 Sol—
Not directly comparable
Toolathlon
Gemini 3.5 Flash56.5%
Source
GPT-5.6 Sol58%
Source
GPT-5.6 Sol leads this result
OSWorld-Verified
Gemini 3.5 Flash78.4%
Source
GPT-5.6 Sol—
Not directly comparable
Finance Agent v2
Gemini 3.5 Flash57.9%
Source
GPT-5.6 Sol—
Not directly comparable
Gert Labs
Gemini 3.5 Flash61.85%
Source
GPT-5.6 Sol—
Not directly comparable
ResearchClawBench
Gemini 3.5 Flash18.0%
Source
GPT-5.6 Sol—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash74.2%
Source
GPT-5.6 Sol85.8%
Source
GPT-5.6 Sol leads this result
Terminal-Bench 3.0
Gemini 3.5 Flash—
GPT-5.6 Sol34.6%
Source
Not directly comparable
BrowseComp
Gemini 3.5 Flash—
GPT-5.6 Sol92.2%
Source
Not directly comparable
OSWorld 2.0
Gemini 3.5 Flash—
GPT-5.6 Sol62.6%
Source
Not directly comparable
CyberGym
Gemini 3.5 Flash—
GPT-5.6 Sol84.5%
Source
Not directly comparable
ExploitGym
Gemini 3.5 Flash—
GPT-5.6 Sol33.7%
Source
Not directly comparable
ApprenticeBench
Gemini 3.5 Flash—
GPT-5.6 Sol26%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
Gemini 3.5 Flash76.2%
Source
GPT-5.6 Sol91.9%
Source
GPT-5.6 Sol leads this result
SWE-bench Pro
Gemini 3.5 Flash55.1%
Source
GPT-5.6 Sol64.6%
Source
GPT-5.6 Sol leads this result
Vibe Code Bench
Gemini 3.5 Flash48.68%
Source
GPT-5.6 Sol—
Not directly comparable
cursorBench31
Gemini 3.5 Flash49.8%
Source
GPT-5.6 Sol—
Not directly comparable
cursorBench32
Shared source
Gemini 3.5 Flash48.8%
GPT-5.6 Sol67.2%
GPT-5.6 Sol leads this result
LiveCodeBench (Vals)
Gemini 3.5 Flash87.6%
Source
GPT-5.6 Sol82.6%
Source
Gemini 3.5 Flash leads this result
SWE-bench (Vals)
Gemini 3.5 Flash78.8%
Source
GPT-5.6 Sol96.2%
Source
GPT-5.6 Sol leads this result
Bug Hunt Bench
Gemini 3.5 Flash—
GPT-5.6 Sol42 fixes
Source
Not directly comparable
DeepSWE
Gemini 3.5 Flash—
GPT-5.6 Sol72.7%
Source
Not directly comparable
FrontierCode 1.1 Extended
Gemini 3.5 Flash—
GPT-5.6 Sol60.6%
Source
Not directly comparable
FrontierSWE v2
Gemini 3.5 Flash—
GPT-5.6 Sol32.2%
Source
Not directly comparable
VulcanBench v3
Gemini 3.5 Flash—
GPT-5.6 Sol87.0%
Source
Not directly comparable
VulcanBench CII v1
Gemini 3.5 Flash—
GPT-5.6 Sol86.5%
Source
Not directly comparable
cursorBench40
Gemini 3.5 Flash—
GPT-5.6 Sol41.7%
Source
Not directly comparable

Reasoning

MRCRv2
Gemini 3.5 Flash77.3%
Source
GPT-5.6 Sol—
Not directly comparable
MRCR 1M
Gemini 3.5 Flash26.6%
Source
GPT-5.6 Sol—
Not directly comparable
ARC-AGI-2
Gemini 3.5 Flash72.1%
Source
GPT-5.6 Sol92.5%
Source
GPT-5.6 Sol leads this result
ARC-AGI-3
Gemini 3.5 Flash—
GPT-5.6 Sol7.8%
Source
Not directly comparable
GeneBench-Pro
Gemini 3.5 Flash—
GPT-5.6 Sol28.7%
Source
Not directly comparable

Multimodal

CharXiv
Gemini 3.5 Flash84.2%
Source
GPT-5.6 Sol—
Not directly comparable
MMMU-Pro
Gemini 3.5 Flash83.6%
Source
GPT-5.6 Sol83%
Source
Gemini 3.5 Flash leads this result
Blueprint-Bench 2
Gemini 3.5 Flash33.6%
Source
GPT-5.6 Sol—
Not directly comparable
MMMU-Pro w/ Python
Gemini 3.5 Flash—
GPT-5.6 Sol84.6%
Source
Not directly comparable

Knowledge

GPQA-D
Gemini 3.5 Flash92.7%
Source
GPT-5.6 Sol94.6%
Source
GPT-5.6 Sol leads this result
HLE
Gemini 3.5 Flash40.2%
Source
GPT-5.6 Sol—
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.5 Flash92.7%
Source
GPT-5.6 Sol95.2%
Source
GPT-5.6 Sol leads this result
MMLU-Pro (Vals)
Gemini 3.5 Flash89.5%
Source
GPT-5.6 Sol89.1%
Source
Gemini 3.5 Flash leads this result
GPQA
Gemini 3.5 Flash—
GPT-5.6 Sol94.6%
Source
Not directly comparable
HLE-Verified
Gemini 3.5 Flash—
GPT-5.6 Sol54.5%
Source
Not directly comparable
LABBench2
Gemini 3.5 Flash—
GPT-5.6 Sol82.1%
Source
Not directly comparable
HealthBench Professional
Gemini 3.5 Flash—
GPT-5.6 Sol60.5%
Source
Not directly comparable
HealthBench Hard
Gemini 3.5 Flash—
GPT-5.6 Sol33.1%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Gemini 3.5 Flash38.966%
Source
GPT-5.6 Sol89.000%
Source
GPT-5.6 Sol leads this result
FrontierMath v2 (Tier 4)
Gemini 3.5 Flash14.583%
Source
GPT-5.6 Sol83.000%
Source
GPT-5.6 Sol leads this result
FrontierMath (legacy)
Gemini 3.5 Flash—
GPT-5.6 Sol89%
Source
Not directly comparable

49 public results · 15 shared

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Last updated September 24, 2026

Gemini 3.5 Flash vs GPT-5.6 Sol

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

Reasoning

Multimodal

Instruction following

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, Gemini 3.5 Flash or GPT-5.6 Sol?

Which is better for coding, Gemini 3.5 Flash or GPT-5.6 Sol?

Which is better for agentic tasks, Gemini 3.5 Flash or GPT-5.6 Sol?

Which costs less, Gemini 3.5 Flash or GPT-5.6 Sol?

Which has the larger context window, Gemini 3.5 Flash or GPT-5.6 Sol?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Gemini 3.5 Flash vs GPT-5.6 Sol