Gemini 3.5 Flash-Lite vs GPT-6 Astra

Updated October 17, 2026. Rank says GPT-6 Astra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-6 Astra has the higher public score, 83.88 versus 49.62, and the 90% score intervals do not overlap. 3 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-Lite

Google

49.62/100

Supported · Public rank #94

90% interval 37.0–62.3

Model B

GPT-6 Astra

OpenAI

83.88/100

Supported · Public rank #3

90% interval 79.4–88.3

Shared results: 3
Gemini 3.5 Flash-Lite only: 9
GPT-6 Astra only: 37
Like-for-like categories: 3 / 8

Supported: Gemini 3.5 Flash-Lite and GPT-6 AstraHow 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 Astra
GPT-6 Astra has the higher public coding point estimate, 75.5 to 35.8, 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-6 Astra
GPT-6 Astra has the higher public agentic point estimate, 74.8 to 35.6, 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-6 Astra
GPT-6 Astra 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-Lite
Gemini 3.5 Flash-Lite 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-Lite
Gemini 3.5 Flash-Lite 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-Lite
Gemini 3.5 Flash-Lite 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.

35.8Gemini 3.5 Flash-Lite75.5GPT-6 Astra

Like-for-like · BenchAlign v5.8

GPT-6 Astra 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.

FrontierMath v2 (Tier 4)Math
Normalized gap 97.6
Gemini 3.5 Flash-Lite:0.000%
GPT-6 Astra:97.600%
Gemini 3.5 Flash-Lite source GPT-6 Astra source
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 67.7
Gemini 3.5 Flash-Lite:25.965%
GPT-6 Astra:93.684%
Shared 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

Gemini 3.5 Flash-Lite: 35.6; Supported · #79/128
GPT-6 Astra: 74.8; Supported · #7/128
Basis: BenchAlign v5.8 lane · 3 vs 14 public rows
Reading: GPT-6 Astra leads

Coding

Like-for-like

Gemini 3.5 Flash-Lite: 35.8; Supported · #76/150
GPT-6 Astra: 75.5; Supported · #4/150
Basis: BenchAlign v5.8 lane · 4 vs 6 public rows
Reading: GPT-6 Astra leads

Knowledge

Like-for-like

Gemini 3.5 Flash-Lite: 49.2; Supported · #84/184
GPT-6 Astra: 86.2; Supported · #3/184
Basis: BenchAlign v5.8 lane · 2 vs 7 public rows
Reading: GPT-6 Astra leads

Reasoning

Not comparable

Gemini 3.5 Flash-Lite: 71.8; Unranked · 3 rankable rows
GPT-6 Astra: 93.3; #1/28
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemini 3.5 Flash-Lite: 80.9; Unranked · 1 rankable row
GPT-6 Astra: 87.3; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.5 Flash-Lite: Not ranked
GPT-6 Astra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.5 Flash-Lite: Not ranked
GPT-6 Astra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.5 Flash-Lite: 37.8; Unranked · 2 rankable rows
GPT-6 Astra: 97.3; Unranked · 2 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-Lite and GPT-6 Astra
Category	Gemini 3.5 Flash-Lite	GPT-6 Astra	Basis	Reading
Agentic	35.6Supported · #79/128	74.8Supported · #7/128	Like-for-likeBenchAlign v5.8 lane · 3 vs 14 public rows	GPT-6 Astra leads
Coding	35.8Supported · #76/150	75.5Supported · #4/150	Like-for-likeBenchAlign v5.8 lane · 4 vs 6 public rows	GPT-6 Astra leads
Knowledge	49.2Supported · #84/184	86.2Supported · #3/184	Like-for-likeBenchAlign v5.8 lane · 2 vs 7 public rows	GPT-6 Astra leads
Reasoning	71.8Unranked · 3 rankable rows	93.3#1/28	Not comparableProvisional lane · 1 vs 2 weighted rows	Not comparable
Multimodal	80.9Unranked · 1 rankable row	87.3Unranked · 3 rankable rows	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	37.8Unranked · 2 rankable rows	97.3Unranked · 2 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

Gemini 3.5 Flash-Lite: $0.00155; Fits in one request
GPT-6 Astra: $0.035; Fits in one request

Gemini 3.5 Flash-Lite has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.5 Flash-Lite: $0.0225; Fits in one request
GPT-6 Astra: $0.65; Fits in one request

Gemini 3.5 Flash-Lite 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-Lite: $0.037; Fits in one request
GPT-6 Astra: $0.9; Fits in one request

Gemini 3.5 Flash-Lite 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 Flash-LiteGPT-6 Astra

Context window

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

Gemini 3.5 Flash-Lite

Google latest Gemini model guide

GPT-6 Astra

1.05M

OpenAI GPT-6 Astra model documentation

API model ID

Gemini 3.5 Flash-Lite

gemini-3.5-flash-lite

Google Gemini 3.5 Flash-Lite model documentation

GPT-6 Astra

gpt-6-astra

OpenAI GPT-6 Astra model documentation

Cached-input rate

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

Gemini 3.5 Flash-Lite

$0.03 per 1M cached input tokens

Google Gemini API pricing

GPT-6 Astra

$1 per 1M cached input tokens

OpenAI GPT-6 Astra model documentation

Documented inputs

Gemini 3.5 Flash-Lite

text, image, video, audio, pdf

Google Gemini 3.5 Flash-Lite model documentation

GPT-6 Astra

text, image

OpenAI GPT-6 Astra model documentation

Documented outputs

Gemini 3.5 Flash-Lite

text

Google Gemini 3.5 Flash-Lite model documentation

GPT-6 Astra

text

OpenAI GPT-6 Astra model documentation

Provider availability

Gemini 3.5 Flash-Lite

Generally Available · Gemini API, Google AI Studio

Google latest Gemini model guide

GPT-6 Astra

Limited Availability · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Plus, Pro, Business, and Enterprise, Microsoft Azure, AWS Bedrock

OpenAI GPT-6 Astra model documentation

Reasoning profile

Gemini 3.5 Flash-Lite

Reasoning

GPT-6 Astra

Reasoning

Weight access

Gemini 3.5 Flash-Lite

Proprietary

GPT-6 Astra

Proprietary

License

Gemini 3.5 Flash-Lite

Proprietary

GPT-6 Astra

Proprietary

Release date

Gemini 3.5 Flash-Lite

2026-07-21

GPT-6 Astra

2026-09-03

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: GPT-6 Astra has the higher public score, 83.88 versus 49.62, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.0225 vs $0.65. Cache-heavy agent loop: $0.037 vs $0.9.
Context tradeoff: GPT-6 Astra 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-Lite or GPT-6 Astra?

GPT-6 Astra has the higher public score, 83.88 versus 49.62, 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-Lite or GPT-6 Astra?

GPT-6 Astra has the higher public coding point estimate, 75.5 to 35.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemini 3.5 Flash-Lite or GPT-6 Astra?

GPT-6 Astra has the higher public agentic tasks point estimate, 74.8 to 35.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Gemini 3.5 Flash-Lite or GPT-6 Astra?

For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.035 on GPT-6 Astra; repository review costs $0.0225 and $0.65; the cache-heavy agent loop costs $0.037 and $0.9. Costs use the listed standard API rates.

Which has the larger context window, Gemini 3.5 Flash-Lite or GPT-6 Astra?

GPT-6 Astra 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 Flash-Lite54.0%
Source
GPT-6 Astra—
Not directly comparable
OSWorld-Verified
Gemini 3.5 Flash-Lite74%
Source
GPT-6 Astra—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash-Lite50.2%
Source
GPT-6 Astra87.3%
Source
GPT-6 Astra leads this result
BrowseComp
Gemini 3.5 Flash-Lite—
GPT-6 Astra91.5%
Source
Not directly comparable
OSWorld 2.0
Gemini 3.5 Flash-Lite—
GPT-6 Astra72.6%
Source
Not directly comparable
Terminal-Bench 4.0
Gemini 3.5 Flash-Lite—
GPT-6 Astra57.90%
Source
Not directly comparable
Terminal-Bench-Science 0.1
Gemini 3.5 Flash-Lite—
GPT-6 Astra64.6%
Source
Not directly comparable
ExploitGym
Gemini 3.5 Flash-Lite—
GPT-6 Astra42.4%
Source
Not directly comparable
Agents' Last Exam
Gemini 3.5 Flash-Lite—
GPT-6 Astra59.3%
Source
Not directly comparable
AutomationBench
Gemini 3.5 Flash-Lite—
GPT-6 Astra41.4%
Source
Not directly comparable
HLE w/ tools
Gemini 3.5 Flash-Lite—
GPT-6 Astra57.2%
Source
Not directly comparable
ApprenticeBench
Gemini 3.5 Flash-Lite—
GPT-6 Astra68%
Source
Not directly comparable
CWE-bench v1
Gemini 3.5 Flash-Lite—
GPT-6 Astra68.0%
Source
Not directly comparable
Hermes Index
Gemini 3.5 Flash-Lite—
GPT-6 Astra56.3%
Source
Not directly comparable
Hermes Bench
Gemini 3.5 Flash-Lite—
GPT-6 Astra73.7%
Source
Not directly comparable
MazeBench
Gemini 3.5 Flash-Lite—
GPT-6 Astra23%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
Gemini 3.5 Flash-Lite54.0%
Source
GPT-6 Astra—
Not directly comparable
SWE-bench Pro
Gemini 3.5 Flash-Lite54.2%
Source
GPT-6 Astra—
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.5 Flash-Lite79.0%
Source
GPT-6 Astra—
Not directly comparable
SWE-bench (Vals)
Gemini 3.5 Flash-Lite75.0%
Source
GPT-6 Astra—
Not directly comparable
DeepSWE
Gemini 3.5 Flash-Lite—
GPT-6 Astra74.1%
Source
Not directly comparable
FrontierCode 1.1 Main
Gemini 3.5 Flash-Lite—
GPT-6 Astra53.3%
Source
Not directly comparable
FrontierCode 1.1 Extended
Gemini 3.5 Flash-Lite—
GPT-6 Astra64.5%
Source
Not directly comparable
FrontierSWE v2
Gemini 3.5 Flash-Lite—
GPT-6 Astra65.5%
Source
Not directly comparable
PostTrainBench v1.1
Gemini 3.5 Flash-Lite—
GPT-6 Astra44.3%
Source
Not directly comparable
Bug Hunt Bench
Gemini 3.5 Flash-Lite—
GPT-6 Astra45.0 fixes
Source
Not directly comparable

Reasoning

MRCRv2
Gemini 3.5 Flash-Lite72.2%
Source
GPT-6 Astra—
Not directly comparable
STEMBench
Gemini 3.5 Flash-Lite—
GPT-6 Astra2.81
Source
Not directly comparable
ARC-AGI-1
Gemini 3.5 Flash-Lite—
GPT-6 Astra98.50%
Source
Not directly comparable
ARC-AGI-2
Gemini 3.5 Flash-Lite—
GPT-6 Astra95%
Source
Not directly comparable
ARC-AGI-3
Gemini 3.5 Flash-Lite—
GPT-6 Astra62.7%
Source
Not directly comparable
GeneBench-Pro
Gemini 3.5 Flash-Lite—
GPT-6 Astra37.8%
Source
Not directly comparable
MRCR v2 256K-512K
Gemini 3.5 Flash-Lite—
GPT-6 Astra100.0%
Source
Not directly comparable
MRCR v2 512K-1M
Gemini 3.5 Flash-Lite—
GPT-6 Astra96.3%
Source
Not directly comparable
GraphWalks BFS 256K–1M
Gemini 3.5 Flash-Lite—
GPT-6 Astra71.8%
Source
Not directly comparable

Multimodal

ScreenSpot Pro
Gemini 3.5 Flash-Lite—
GPT-6 Astra92.7%
Source
Not directly comparable
BenchCAD Vision2Code (tools)
Gemini 3.5 Flash-Lite—
GPT-6 Astra0.959
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
Gemini 3.5 Flash-Lite83.8%
Source
GPT-6 Astra—
Not directly comparable
MMLU-Pro (Vals)
Gemini 3.5 Flash-Lite85.8%
Source
GPT-6 Astra—
Not directly comparable
GPQA
Gemini 3.5 Flash-Lite—
GPT-6 Astra96%
Source
Not directly comparable
GPQA-D
Gemini 3.5 Flash-Lite—
GPT-6 Astra96.0%
Source
Not directly comparable
HealthBench (raw)
Gemini 3.5 Flash-Lite—
GPT-6 Astra56.9%
Source
Not directly comparable
HealthBench (length-adjusted)
Gemini 3.5 Flash-Lite—
GPT-6 Astra58.3%
Source
Not directly comparable
HealthBench Professional
Gemini 3.5 Flash-Lite—
GPT-6 Astra64.7%
Source
Not directly comparable
HealthBench Professional (raw)
Gemini 3.5 Flash-Lite—
GPT-6 Astra68.2%
Source
Not directly comparable
HealthBench Hard
Gemini 3.5 Flash-Lite—
GPT-6 Astra36.6%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Gemini 3.5 Flash-Lite—
GPT-6 Astra8.5%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Shared source
Gemini 3.5 Flash-Lite25.965%
GPT-6 Astra93.684%
GPT-6 Astra leads this result
FrontierMath v2 (Tier 4)
Gemini 3.5 Flash-Lite0.000%
Source
GPT-6 Astra97.600%
Source
GPT-6 Astra leads this result

49 public results · 3 shared

Related comparisons

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Last updated October 17, 2026

Gemini 3.5 Flash-Lite vs GPT-6 Astra

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

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 3.5 Flash-Lite or GPT-6 Astra?

Which is better for coding, Gemini 3.5 Flash-Lite or GPT-6 Astra?

Which is better for agentic tasks, Gemini 3.5 Flash-Lite or GPT-6 Astra?

Which costs less, Gemini 3.5 Flash-Lite or GPT-6 Astra?

Which has the larger context window, Gemini 3.5 Flash-Lite or GPT-6 Astra?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Gemini 3.5 Flash-Lite vs GPT-6 Astra