Gemini 3.8 Flash vs GPT-6.1 Sol

Updated September 29, 2026. Rank says Gemini 3.8 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Gemini 3.8 Flash has the higher public score estimate, 73.31 versus 66.86, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 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 3.8 Flash

Google

73.31/100

Supported · Public rank #10

90% interval 68.4–78.3

Model B

GPT-6.1 Sol

OpenAI

66.86/100

Estimated · Public rank #23

90% interval 55.4–78.4

Shared results: 1
Gemini 3.8 Flash only: 23
GPT-6.1 Sol only: 8
Like-for-like categories: 1 / 8

Supported: Gemini 3.8 Flash · Estimated: GPT-6.1 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-6.1 Sol
GPT-6.1 Sol leads on the public coding lane, 67 to 64.2, 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
Chat turn cost
1K fresh input + 500 output tokens
Gemini 3.8 Flash
Gemini 3.8 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

Show secondary and unsupported calls

Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 3.8 Flash
Gemini 3.8 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 3.8 Flash
Gemini 3.8 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
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

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.

64.2Gemini 3.8 Flash67.0GPT-6.1 Sol

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Gemini 3.8 Flash: 64.2; Supported · #11/143
GPT-6.1 Sol: 67.0; Supported · #8/143
Basis: BenchAlign v5.7 lane · 7 vs 1 public rows
Reading: GPT-6.1 Sol leads · intervals overlap

Knowledge

Directional only

Gemini 3.8 Flash: 73.8; Supported · #9/169
GPT-6.1 Sol: 71.3; Estimated · #10/169
Basis: BenchAlign v5.7 lane · 6 vs 5 public rows
Reading: Directional only

Agentic

Not comparable

Gemini 3.8 Flash: 65.6; Supported · #11/117
GPT-6.1 Sol: Not ranked
Basis: BenchAlign v5.7 lane · 6 vs 3 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemini 3.8 Flash: 70.6; #15/27
GPT-6.1 Sol: 79.4; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemini 3.8 Flash: 83.9; #9/50
GPT-6.1 Sol: 83.9; Unranked · 1 rankable row
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.8 Flash: Not ranked
GPT-6.1 Sol: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.8 Flash: Not ranked
GPT-6.1 Sol: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.8 Flash: Not ranked
GPT-6.1 Sol: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 3.8 Flash and GPT-6.1 Sol
Category	Gemini 3.8 Flash	GPT-6.1 Sol	Basis	Reading
Coding	64.2Supported · #11/143	67.0Supported · #8/143	Like-for-likeBenchAlign v5.7 lane · 7 vs 1 public rows	GPT-6.1 Sol leads · intervals overlap
Knowledge	73.8Supported · #9/169	71.3Estimated · #10/169	Directional onlyBenchAlign v5.7 lane · 6 vs 5 public rows	Directional only
Agentic	65.6Supported · #11/117	Not ranked	Not comparableBenchAlign v5.7 lane · 6 vs 3 public rows	Not comparable
Reasoning	70.6#15/27	79.4Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multimodal	83.9#9/50	83.9Unranked · 1 rankable row	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 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

Gemini 3.8 Flash: $0.00262; Fits in one request
GPT-6.1 Sol: $0.007; Fits in one request

Gemini 3.8 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.8 Flash: $0.04875; Fits in one request
GPT-6.1 Sol: $0.13; Fits in one request

Gemini 3.8 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 3.8 Flash: $0.0675; Fits in one request
GPT-6.1 Sol: $0.16; Fits in one request

Gemini 3.8 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 3.8 FlashGPT-6.1 Sol

Context window

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

Gemini 3.8 Flash

Google Gemini 3.8 Flash API documentation

GPT-6.1 Sol

1.05M

OpenAI GPT-6.1 Sol model documentation

API model ID

Gemini 3.8 Flash

gemini-3.8-flash

Google Gemini 3.8 Flash API documentation

GPT-6.1 Sol

gpt-6.1-sol

OpenAI GPT-6.1 Sol model documentation

Cached-input rate

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

Gemini 3.8 Flash

$0.075 per 1M cached input tokens

Google Gemini API pricing

GPT-6.1 Sol

$0.1 per 1M cached input tokens

OpenAI GPT-6.1 Sol model documentation

Documented inputs

Gemini 3.8 Flash

text, image, video, audio, pdf

Google Gemini 3.8 Flash API documentation

GPT-6.1 Sol

text, image

OpenAI GPT-6.1 Sol model documentation

Documented outputs

Gemini 3.8 Flash

text

Google Gemini 3.8 Flash API documentation

GPT-6.1 Sol

text

OpenAI GPT-6.1 Sol model documentation

Provider availability

Gemini 3.8 Flash

Generally Available · Gemini API, Google AI Studio, Android Studio, Stitch, Gemini app, Gemini Enterprise, Google AI Mode, Google Sheets, Google Antigravity

Google Gemini 3.8 Flash and Gemini 3.8 Flash Cyber launch

GPT-6.1 Sol

Generally Available · OpenAI Responses API, OpenAI Chat Completions API

OpenAI GPT-6.1 Sol model documentation

Reasoning profile

Gemini 3.8 Flash

Reasoning

GPT-6.1 Sol

Reasoning

Weight access

Gemini 3.8 Flash

Proprietary

GPT-6.1 Sol

Proprietary

License

Gemini 3.8 Flash

Proprietary

GPT-6.1 Sol

Proprietary

Release date

Gemini 3.8 Flash

2026-09-02

GPT-6.1 Sol

2026-09-29

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: Gemini 3.8 Flash has the higher public score estimate, 73.31 versus 66.86, but the 90% score intervals overlap.
Workload cost: Repository review: $0.04875 vs $0.13. Cache-heavy agent loop: $0.0675 vs $0.16.
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, Gemini 3.8 Flash or GPT-6.1 Sol?

Gemini 3.8 Flash has the higher public score estimate, 73.31 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, Gemini 3.8 Flash or GPT-6.1 Sol?

GPT-6.1 Sol leads the public coding lane, 67 to 64.2, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Gemini 3.8 Flash or GPT-6.1 Sol?

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, Gemini 3.8 Flash or GPT-6.1 Sol?

For the stated presets, chat costs $0.00263 on Gemini 3.8 Flash and $0.007 on GPT-6.1 Sol; repository review costs $0.04875 and $0.13; the cache-heavy agent loop costs $0.0675 and $0.16. Costs use the listed standard API rates.

Which has the larger context window, Gemini 3.8 Flash or GPT-6.1 Sol?

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 evidence32 rows

Agentic

Finance Agent v2
Gemini 3.8 Flash61.4%
Source
GPT-6.1 Sol—
Not directly comparable
Terminal-Bench 2.1
Gemini 3.8 Flash89.4%
Source
GPT-6.1 Sol—
Not directly comparable
Terminal-Bench 4.0
Gemini 3.8 Flash19.10%
Source
GPT-6.1 Sol—
Not directly comparable
OSWorld 2.0
Gemini 3.8 Flash59.0%
Source
GPT-6.1 Sol—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.8 Flash81.3%
Source
GPT-6.1 Sol—
Not directly comparable
ApprenticeBench
Gemini 3.8 Flash24%
Source
GPT-6.1 Sol—
Not directly comparable
AutomationBench
Gemini 3.8 Flash—
GPT-6.1 Sol36.1%
Source
Not directly comparable
Terminal-Bench-Science 0.1
Gemini 3.8 Flash—
GPT-6.1 Sol57.0%
Source
Not directly comparable
ExploitGym
Gemini 3.8 Flash—
GPT-6.1 Sol35.1%
Source
Not directly comparable

Coding

DeepSWE
Gemini 3.8 Flash73.8%
Source
GPT-6.1 Sol71.9%
Source
Gemini 3.8 Flash leads this result
Terminal-Bench 2.1
Gemini 3.8 Flash89.4%
Source
GPT-6.1 Sol—
Not directly comparable
cursorBench32
Gemini 3.8 Flash69.2%
Source
GPT-6.1 Sol—
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.8 Flash89.5%
Source
GPT-6.1 Sol—
Not directly comparable
SWE-bench (Vals)
Gemini 3.8 Flash80.0%
Source
GPT-6.1 Sol—
Not directly comparable
FrontierSWE v2
Gemini 3.8 Flash19.6%
Source
GPT-6.1 Sol—
Not directly comparable
cursorBench40
Gemini 3.8 Flash39.6%
Source
GPT-6.1 Sol—
Not directly comparable

Reasoning

ARC-AGI-1
Gemini 3.8 Flash98.50%
Source
GPT-6.1 Sol—
Not directly comparable
ARC-AGI-2
Gemini 3.8 Flash89.2%
Source
GPT-6.1 Sol—
Not directly comparable
ARC-AGI-3
Gemini 3.8 Flash10.4%
Source
GPT-6.1 Sol—
Not directly comparable

Multimodal

CharXiv w/o tools
Gemini 3.8 Flash86.2%
Source
GPT-6.1 Sol—
Not directly comparable
LVBench
Gemini 3.8 Flash87.1%
Source
GPT-6.1 Sol—
Not directly comparable

Knowledge

HLE-Verified
Gemini 3.8 Flash54.9%
Source
GPT-6.1 Sol—
Not directly comparable
LABBench2
Gemini 3.8 Flash86.2%
Source
GPT-6.1 Sol—
Not directly comparable
BioMysteryBench (human-solvable)
Gemini 3.8 Flash88.8%
Source
GPT-6.1 Sol—
Not directly comparable
BioMysteryBench (human-difficult)
Gemini 3.8 Flash56.5%
Source
GPT-6.1 Sol—
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.8 Flash94.4%
Source
GPT-6.1 Sol—
Not directly comparable
MMLU-Pro (Vals)
Gemini 3.8 Flash90.2%
Source
GPT-6.1 Sol—
Not directly comparable
HealthBench (raw)
Gemini 3.8 Flash—
GPT-6.1 Sol56.7%
Source
Not directly comparable
HealthBench (length-adjusted)
Gemini 3.8 Flash—
GPT-6.1 Sol58.5%
Source
Not directly comparable
HealthBench Professional
Gemini 3.8 Flash—
GPT-6.1 Sol64.2%
Source
Not directly comparable
HealthBench Professional (raw)
Gemini 3.8 Flash—
GPT-6.1 Sol67.2%
Source
Not directly comparable
HealthBench Hard
Gemini 3.8 Flash—
GPT-6.1 Sol36.2%
Source
Not directly comparable

32 public results · 1 shared

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Last updated September 29, 2026

Gemini 3.8 Flash vs GPT-6.1 Sol

Decision reading

Which one for your work

Coding work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Agentic work

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, Gemini 3.8 Flash or GPT-6.1 Sol?

Which is better for coding, Gemini 3.8 Flash or GPT-6.1 Sol?

Which is better for agentic tasks, Gemini 3.8 Flash or GPT-6.1 Sol?

Which costs less, Gemini 3.8 Flash or GPT-6.1 Sol?

Which has the larger context window, Gemini 3.8 Flash or GPT-6.1 Sol?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Related comparisons

Watch Gemini 3.8 Flash vs GPT-6.1 Sol