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Gemini 3.1 Pro 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.

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Decision reading

GPT-6 Astra has the higher public point estimate, 83.88 versus 64.7. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo
Gemini 3.1 Pro

Google

64.7/100

Estimated · Public rank #37

Conditional range 55.0–74.4

Model B
OpenAI logo
GPT-6 Astra

OpenAI

83.88/100

Supported · Public rank #3

90% interval 79.4–88.3

Shared results
10
Gemini 3.1 Pro only
20
GPT-6 Astra only
30
Like-for-like categories
3 / 8
Estimated: Gemini 3.1 Pro · Supported: GPT-6 Astra. 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-6 Astra

    GPT-6 Astra has the higher public coding point estimate, 75.5 to 39.2, 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 38.4, 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-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 3.1 Pro

    Gemini 3.1 Pro 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 3.1 Pro

    Gemini 3.1 Pro 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 3.1 Pro

    Gemini 3.1 Pro 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.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

39.2Gemini 3.1 Pro75.5GPT-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 leaderboardShortlist models for this job
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.

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.

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 3.1 Pro
38.4
Supported · #70/128
GPT-6 Astra
74.8
Supported · #7/128
Basis
BenchAlign v5.8 lane · 7 vs 14 public rows
Reading
GPT-6 Astra leads

Coding

Like-for-like
Gemini 3.1 Pro
39.2
Supported · #67/150
GPT-6 Astra
75.5
Supported · #4/150
Basis
BenchAlign v5.8 lane · 6 vs 6 public rows
Reading
GPT-6 Astra leads

Knowledge

Like-for-like
Gemini 3.1 Pro
67.6
Supported · #26/184
GPT-6 Astra
86.2
Supported · #3/184
Basis
BenchAlign v5.8 lane · 6 vs 7 public rows
Reading
GPT-6 Astra leads · intervals overlap

Reasoning

Not comparable
Gemini 3.1 Pro
58.2
Unranked · 2 rankable rows
GPT-6 Astra
93.3
#1/28
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.1 Pro
80.1
#17/54
GPT-6 Astra
87.3
Unranked · 3 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.1 Pro
Not ranked
GPT-6 Astra
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.1 Pro
Not ranked
GPT-6 Astra
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3.1 Pro
65.0
Unranked · 2 rankable rows
GPT-6 Astra
97.3
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
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 3.1 Pro
$0.008
Fits in one request
GPT-6 Astra
$0.035
Fits in one request

Gemini 3.1 Pro has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.1 Pro
$0.136
Fits in one request
GPT-6 Astra
$0.65
Fits in one request

Gemini 3.1 Pro 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 3.1 Pro
$0.2
Fits in one request
GPT-6 Astra
$0.9
Fits in one request

Gemini 3.1 Pro 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.

Reasoning profile

Gemini 3.1 Pro

Reasoning

GPT-6 Astra

Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

GPT-6 Astra

Proprietary

License

Gemini 3.1 Pro

Proprietary

GPT-6 Astra

Proprietary

Release date

Gemini 3.1 Pro

2026-02-19

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 point estimate, 83.88 versus 64.7. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost
Repository review: $0.136 vs $0.65. Cache-heavy agent loop: $0.2 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 3.1 Pro or GPT-6 Astra?

GPT-6 Astra has the higher public point estimate, 83.88 versus 64.7. 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, Gemini 3.1 Pro or GPT-6 Astra?

GPT-6 Astra has the higher public coding point estimate, 75.5 to 39.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemini 3.1 Pro or GPT-6 Astra?

GPT-6 Astra has the higher public agentic tasks point estimate, 74.8 to 38.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Gemini 3.1 Pro or GPT-6 Astra?

For the stated presets, chat costs $0.008 on Gemini 3.1 Pro and $0.035 on GPT-6 Astra; repository review costs $0.136 and $0.65; the cache-heavy agent loop costs $0.2 and $0.9. Costs use the listed standard API rates.

Which has the larger context window, Gemini 3.1 Pro 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 evidence60 rows

Agentic

  • Claw-Eval

    Gemini 3.1 Pro57.8%
    Source
    GPT-6 Astra—

    Not directly comparable

  • DeepSearchQA

    Gemini 3.1 Pro69.7%
    Source
    GPT-6 Astra—

    Not directly comparable

  • τ²-bench results

    Gemini 3.1 Pro95.6%
    Source
    GPT-6 Astra—

    Not directly comparable

  • Gert Labs

    Gemini 3.1 Pro56.87%
    Source
    GPT-6 Astra—

    Not directly comparable

  • ResearchClawBench

    Gemini 3.1 Pro13.3%
    Source
    GPT-6 Astra—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.1 Pro70.8%
    Source
    GPT-6 Astra87.3%
    Source

    GPT-6 Astra leads this result

  • MazeBench

    Gemini 3.1 Pro1%
    Source
    GPT-6 Astra23%
    Source

    GPT-6 Astra leads this result

  • BrowseComp

    Gemini 3.1 Pro—
    GPT-6 Astra91.5%
    Source

    Not directly comparable

  • OSWorld 2.0

    Gemini 3.1 Pro—
    GPT-6 Astra72.6%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    Gemini 3.1 Pro—
    GPT-6 Astra57.90%
    Source

    Not directly comparable

  • Terminal-Bench-Science 0.1

    Gemini 3.1 Pro—
    GPT-6 Astra64.6%
    Source

    Not directly comparable

  • ExploitGym

    Gemini 3.1 Pro—
    GPT-6 Astra42.4%
    Source

    Not directly comparable

  • Agents' Last Exam

    Gemini 3.1 Pro—
    GPT-6 Astra59.3%
    Source

    Not directly comparable

  • AutomationBench

    Gemini 3.1 Pro—
    GPT-6 Astra41.4%
    Source

    Not directly comparable

  • HLE w/ tools

    Gemini 3.1 Pro—
    GPT-6 Astra57.2%
    Source

    Not directly comparable

  • ApprenticeBench

    Gemini 3.1 Pro—
    GPT-6 Astra68%
    Source

    Not directly comparable

  • CWE-bench v1

    Gemini 3.1 Pro—
    GPT-6 Astra68.0%
    Source

    Not directly comparable

  • Hermes Index

    Gemini 3.1 Pro—
    GPT-6 Astra56.3%
    Source

    Not directly comparable

  • Hermes Bench

    Gemini 3.1 Pro—
    GPT-6 Astra73.7%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pro

    Gemini 3.1 Pro82.9%
    Source
    GPT-6 Astra—

    Not directly comparable

  • React Native Evals

    Gemini 3.1 Pro78.9%
    Source
    GPT-6 Astra—

    Not directly comparable

  • Vibe Code Bench

    Gemini 3.1 Pro32.03%
    Source
    GPT-6 Astra—

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 3.1 Pro88.5%
    Source
    GPT-6 Astra—

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 3.1 Pro78.8%
    Source
    GPT-6 Astra—

    Not directly comparable

  • PostTrainBench v1.1

    Gemini 3.1 Pro22.0%
    Source
    GPT-6 Astra44.3%
    Source

    GPT-6 Astra leads this result

  • DeepSWE

    Gemini 3.1 Pro—
    GPT-6 Astra74.1%
    Source

    Not directly comparable

  • FrontierCode 1.1 Main

    Gemini 3.1 Pro—
    GPT-6 Astra53.3%
    Source

    Not directly comparable

  • FrontierCode 1.1 Extended

    Gemini 3.1 Pro—
    GPT-6 Astra64.5%
    Source

    Not directly comparable

  • FrontierSWE v2

    Gemini 3.1 Pro—
    GPT-6 Astra65.5%
    Source

    Not directly comparable

  • Bug Hunt Bench

    Gemini 3.1 Pro—
    GPT-6 Astra45.0 fixes
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 3.1 Pro77.1%
    Source
    GPT-6 Astra95%
    Source

    GPT-6 Astra leads this result

  • ARC-AGI-3

    Gemini 3.1 Pro0.4%
    Source
    GPT-6 Astra62.7%
    Source

    GPT-6 Astra leads this result

  • STEMBench

    Gemini 3.1 Pro—
    GPT-6 Astra2.81
    Source

    Not directly comparable

  • ARC-AGI-1

    Gemini 3.1 Pro—
    GPT-6 Astra98.50%
    Source

    Not directly comparable

  • GeneBench-Pro

    Gemini 3.1 Pro—
    GPT-6 Astra37.8%
    Source

    Not directly comparable

  • MRCR v2 256K-512K

    Gemini 3.1 Pro—
    GPT-6 Astra100.0%
    Source

    Not directly comparable

  • MRCR v2 512K-1M

    Gemini 3.1 Pro—
    GPT-6 Astra96.3%
    Source

    Not directly comparable

  • GraphWalks BFS 256K–1M

    Gemini 3.1 Pro—
    GPT-6 Astra71.8%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 3.1 Pro83.9%
    Source
    GPT-6 Astra—

    Not directly comparable

  • CharXiv

    Gemini 3.1 Pro80.2%
    Source
    GPT-6 Astra—

    Not directly comparable

  • ERQA

    Gemini 3.1 Pro69.4%
    Source
    GPT-6 Astra—

    Not directly comparable

  • SimpleVQA

    Gemini 3.1 Pro72.4%
    Source
    GPT-6 Astra—

    Not directly comparable

  • ScreenSpot Pro

    Gemini 3.1 Pro84.4%
    Source
    GPT-6 Astra92.7%
    Source

    GPT-6 Astra leads this result

  • ZeroBench

    Gemini 3.1 Pro29.0%
    Source
    GPT-6 Astra—

    Not directly comparable

  • MedXpertQA (MM)

    Gemini 3.1 Pro81.3%
    Source
    GPT-6 Astra—

    Not directly comparable

  • BenchCAD Vision2Code (tools)

    Gemini 3.1 Pro—
    GPT-6 Astra0.959
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.1 Pro94.3%
    Source
    GPT-6 Astra96.0%
    Source

    GPT-6 Astra leads this result

  • HLE w/o tools

    Gemini 3.1 Pro45.4%
    Source
    GPT-6 Astra—

    Not directly comparable

  • HealthBench Hard

    Gemini 3.1 Pro20.6%
    Source
    GPT-6 Astra36.6%
    Source

    GPT-6 Astra leads this result

  • MedXpertQA (Text)

    Gemini 3.1 Pro71.5%
    Source
    GPT-6 Astra—

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemini 3.1 Pro95.5%
    Source
    GPT-6 Astra—

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 3.1 Pro91.0%
    Source
    GPT-6 Astra—

    Not directly comparable

  • GPQA

    Gemini 3.1 Pro—
    GPT-6 Astra96%
    Source

    Not directly comparable

  • HealthBench (raw)

    Gemini 3.1 Pro—
    GPT-6 Astra56.9%
    Source

    Not directly comparable

  • HealthBench (length-adjusted)

    Gemini 3.1 Pro—
    GPT-6 Astra58.3%
    Source

    Not directly comparable

  • HealthBench Professional

    Gemini 3.1 Pro—
    GPT-6 Astra64.7%
    Source

    Not directly comparable

  • HealthBench Professional (raw)

    Gemini 3.1 Pro—
    GPT-6 Astra68.2%
    Source

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Gemini 3.1 Pro—
    GPT-6 Astra8.5%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Gemini 3.1 Pro59.649%
    GPT-6 Astra93.684%

    GPT-6 Astra leads this result

  • FrontierMath v2 (Tier 4)

    Gemini 3.1 Pro26.829%
    Source
    GPT-6 Astra97.600%
    Source

    GPT-6 Astra leads this result

60 public results · 10 shared

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Last updated October 17, 2026