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Radar

Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.

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BenchLM
OpenAI logo
Model A
GPT-4.1 nano

OpenAI

42.64/100

Estimated · Public rank #192

90% interval 31.1–54.2

GPT-4.1 nano vs GPT-6 Astra

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

OpenAI logo
Model B
GPT-6 Astra

OpenAI

81.88/100

Estimated · Public rank #5

90% interval 70.4–93.4

Decision reading

GPT-6 Astra has the higher public score, 81.88 versus 42.64, 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-4.1 nano

    GPT-4.1 nano 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

    GPT-4.1 nano

    GPT-4.1 nano has the lower estimated token cost for this stated workload. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-4.1 nano

    GPT-4.1 nano 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

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-4.1 nano only
3
GPT-6 Astra only
15
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-4.1 nano
50.3
GPT-6 Astra
96.0
Weighted basis
1 vs 1 rows
Reading
GPT-6 Astra leads

Agentic

Not comparable
GPT-4.1 nano
Not measured
GPT-6 Astra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-4.1 nano
Not measured
GPT-6 Astra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-4.1 nano
Not measured
GPT-6 Astra
95.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
GPT-4.1 nano
1.0
GPT-6 Astra
97.6
Weighted basis
1 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-4.1 nano
Not measured
GPT-6 Astra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-4.1 nano
Not measured
GPT-6 Astra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-4.1 nano
83.2
GPT-6 Astra
Not measured
Weighted basis
1 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-4.1 nano
$0.0003
Fits in one request
GPT-6 Astra
$0.035
Fits in one request

GPT-4.1 nano has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-4.1 nano
$0.0062
Fits in one request
GPT-6 Astra
$0.65
Fits in one request

GPT-4.1 nano 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-4.1 nano
$0.026
Fits in one request
Cached input priced at the published list-input rate
GPT-6 Astra
$0.9
Fits in one request

GPT-4.1 nano has the lower modeled cost

GPT-4.1 nano 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-4.1 nano

Not published

GPT-6 Astra

$1 per 1M cached input tokens

OpenAI GPT-6 Astra model documentation

Reasoning profile

GPT-4.1 nano

Non-Reasoning

GPT-6 Astra

Reasoning

Weight access

GPT-4.1 nano

Proprietary

GPT-6 Astra

Proprietary

License

GPT-4.1 nano

Proprietary

GPT-6 Astra

Proprietary

Release date

GPT-4.1 nano

2025-04-14

GPT-6 Astra

2026-09-03

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 42.64, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.0062 vs $0.65. Cache-heavy agent loop: $0.026 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.

Benchmark evidence

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

Browse raw public benchmark evidence19 rows

Agentic

  • OSWorld 2.0

    GPT-4.1 nano—
    GPT-6 Astra72.6%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    GPT-4.1 nano—
    GPT-6 Astra57.70%
    Source

    Not directly comparable

  • Terminal-Bench-Science 0.1

    GPT-4.1 nano—
    GPT-6 Astra64.6%
    Source

    Not directly comparable

  • ExploitGym

    GPT-4.1 nano—
    GPT-6 Astra42.4%
    Source

    Not directly comparable

  • Agents' Last Exam

    GPT-4.1 nano—
    GPT-6 Astra59.3%
    Source

    Not directly comparable

Coding

  • deepSwe

    GPT-4.1 nano—
    GPT-6 Astra74.1%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-4.1 nano—
    GPT-6 Astra95%
    Source

    Not directly comparable

  • ARC-AGI-3

    GPT-4.1 nano—
    GPT-6 Astra62.7%
    Source

    Not directly comparable

  • MRCR v2 256K-512K

    GPT-4.1 nano—
    GPT-6 Astra100.0%
    Source

    Not directly comparable

  • MRCR v2 512K-1M

    GPT-4.1 nano—
    GPT-6 Astra96.3%
    Source

    Not directly comparable

Knowledge

  • MMLU

    GPT-4.1 nano80.1%
    Source
    GPT-6 Astra—

    Not directly comparable

  • GPQA

    GPT-4.1 nano50.3%
    Source
    GPT-6 Astra96%
    Source

    GPT-6 Astra leads this result

  • GPQA-D

    GPT-4.1 nano—
    GPT-6 Astra96.0%
    Source

    Not directly comparable

  • HealthBench Professional

    GPT-4.1 nano—
    GPT-6 Astra63.4%
    Source

    Not directly comparable

  • HealthBench Hard

    GPT-4.1 nano—
    GPT-6 Astra36.3%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-4.1 nano1.034%
    Source
    GPT-6 Astra—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-4.1 nano—
    GPT-6 Astra97.600%
    Source

    Not directly comparable

Multimodal

  • ScreenSpot Pro

    GPT-4.1 nano—
    GPT-6 Astra92.7%
    Source

    Not directly comparable

Instruction following

  • IFEval

    GPT-4.1 nano83.2%
    Source
    GPT-6 Astra—

    Not directly comparable

Frequently asked questions

Which is better, GPT-4.1 nano or GPT-6 Astra?

GPT-6 Astra has the higher public score, 81.88 versus 42.64, 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-4.1 nano or GPT-6 Astra?

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-4.1 nano or GPT-6 Astra?

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-4.1 nano or GPT-6 Astra?

For the stated presets, chat costs $0.0003 on GPT-4.1 nano and $0.035 on GPT-6 Astra; repository review costs $0.0062 and $0.65; the cache-heavy agent loop costs $0.026 and $0.9. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-4.1 nano or GPT-6 Astra?

GPT-6 Astra has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 3, 2026

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