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Radar

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Model A
GPT-6 Astra

OpenAI

81.88/100

Estimated · Public rank #5

90% interval 70.493.4

GPT-6 Astra vs o1-pro

Updated September 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

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Model B
o1-pro

OpenAI

46.29/100

Estimated · Public rank #168

90% interval 34.857.8

Decision reading

GPT-6 Astra has the higher public score, 81.88 versus 46.29, and the 90% score intervals do not overlap.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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.

  • Long documents

    Prompts that approach the documented context limit

    GPT-6 Astra

    GPT-6 Astra has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-6 Astra

    GPT-6 Astra 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
  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-6 Astra

    GPT-6 Astra has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o1-pro does not fit this workload in one request. o1-pro has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
1
GPT-6 Astra only
15
o1-pro only
0
Like-for-like categories
1 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Knowledge

Like-for-like
GPT-6 Astra
96.0
o1-pro
79.0
Weighted basis
1 vs 1 rows
Reading
GPT-6 Astra leads

Agentic

Not comparable
GPT-6 Astra
Not measured
o1-pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-6 Astra
Not measured
o1-pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-6 Astra
95.0
o1-pro
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-6 Astra
97.6
o1-pro
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-6 Astra
Not measured
o1-pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-6 Astra
Not measured
o1-pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-6 Astra
Not measured
o1-pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

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-6 Astra
$0.035
Fits in one request
o1-pro
$0.45
Fits in one request

GPT-6 Astra has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-6 Astra
$0.65
Fits in one request
o1-pro
$9.30
Fits in one request

GPT-6 Astra has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GPT-6 Astra
$0.9
Fits in one request
o1-pro
$39.00
Does not fit in one request
Cached input priced at the published list-input rate

o1-pro does not fit this workload in one request. o1-pro has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

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

GPT-6 Astra

$1 per 1M cached input tokens

OpenAI GPT-6 Astra model documentation

o1-pro

Not published

Reasoning profile

GPT-6 Astra

Reasoning

o1-pro

Reasoning

Weight access

GPT-6 Astra

Proprietary

o1-pro

Proprietary

License

GPT-6 Astra

Proprietary

o1-pro

Proprietary

Release date

GPT-6 Astra

2026-09-03

o1-pro

2024-12-01

If you already use one of these models
Deployment change
Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
GPT-6 Astra has the higher public score, 81.88 versus 46.29, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.65 vs $9.30. Cache-heavy agent loop: $0.9 vs $39.00.
Context tradeoff
GPT-6 Astra has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

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

Browse raw public benchmark evidence16 rows

Agentic

  • OSWorld 2.0

    GPT-6 Astra72.6%
    Source
    o1-pro

    Not directly comparable

  • Terminal-Bench 4.0

    GPT-6 Astra57.70%
    Source
    o1-pro

    Not directly comparable

  • Terminal-Bench-Science 0.1

    GPT-6 Astra64.6%
    Source
    o1-pro

    Not directly comparable

  • ExploitGym

    GPT-6 Astra42.4%
    Source
    o1-pro

    Not directly comparable

  • Agents' Last Exam

    GPT-6 Astra59.3%
    Source
    o1-pro

    Not directly comparable

Coding

  • deepSwe

    GPT-6 Astra74.1%
    Source
    o1-pro

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-6 Astra95%
    Source
    o1-pro

    Not directly comparable

  • ARC-AGI-3

    GPT-6 Astra62.7%
    Source
    o1-pro

    Not directly comparable

  • MRCR v2 256K-512K

    GPT-6 Astra100.0%
    Source
    o1-pro

    Not directly comparable

  • MRCR v2 512K-1M

    GPT-6 Astra96.3%
    Source
    o1-pro

    Not directly comparable

Knowledge

  • GPQA

    GPT-6 Astra96%
    Source
    o1-pro79%
    Source

    GPT-6 Astra leads this result

  • GPQA-D

    GPT-6 Astra96.0%
    Source
    o1-pro

    Not directly comparable

  • HealthBench Professional

    GPT-6 Astra63.4%
    Source
    o1-pro

    Not directly comparable

  • HealthBench Hard

    GPT-6 Astra36.3%
    Source
    o1-pro

    Not directly comparable

Math

  • FrontierMath v2 (Tier 4)

    GPT-6 Astra97.600%
    Source
    o1-pro

    Not directly comparable

Multimodal

  • ScreenSpot Pro

    GPT-6 Astra92.7%
    Source
    o1-pro

    Not directly comparable

Frequently asked questions

Which is better, GPT-6 Astra or o1-pro?

GPT-6 Astra has the higher public score, 81.88 versus 46.29, 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, GPT-6 Astra or o1-pro?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, GPT-6 Astra or o1-pro?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, GPT-6 Astra or o1-pro?

For the stated presets, chat costs $0.035 on GPT-6 Astra and $0.45 on o1-pro; repository review costs $0.65 and $9.30; the cache-heavy agent loop costs $0.9 and $39.00. o1-pro does not fit this workload in one request. o1-pro has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-6 Astra or o1-pro?

GPT-6 Astra has the larger documented context window: 1.05M, compared with 200K.

Related comparisons

Last updated September 3, 2026

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