Skip to main content
BenchLM
Data

GPT-6 Astra vs Muse Wonder Full

Updated October 12, 2026. Rank says GPT-6 Astra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Share or export
Share on XLinkedInAPI/MCP

Decision reading

GPT-6 Astra has the higher public point estimate, 83.26 versus 73.81. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
OpenAI logo

OpenAI

83.26/100

Supported · Public rank #2

90% interval 78.8–87.7

Model B
Meta logo

Meta

73.81/100

Estimated · Public rank #11

Conditional range 59.5–88.2

Shared results
2
GPT-6 Astra only
33
Muse Wonder Full only
4
Like-for-like categories
2 / 8
Supported: GPT-6 Astra · Estimated: Muse Wonder Full. 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, 75.7 to 74, 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
  • Agentic work

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

    Not enough matched evidence

    Muse Wonder Full is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited
  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates
  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates
  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    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.

75.7GPT-6 Astra74.0Muse Wonder Full

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.

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.

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 v5.8 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.

Coding

Like-for-like
GPT-6 Astra
75.7
Supported · #4/148
Muse Wonder Full
74.0
Supported · #5/148
Basis
BenchAlign v5.8 lane · 6 vs 3 public rows
Reading
GPT-6 Astra leads · intervals overlap

Knowledge

Like-for-like
GPT-6 Astra
85.5
Supported · #3/180
Muse Wonder Full
77.9
Supported · #10/180
Basis
BenchAlign v5.8 lane · 7 vs 1 public rows
Reading
GPT-6 Astra leads · intervals overlap

Agentic

Not comparable
GPT-6 Astra
70.7
Supported · #7/123
Muse Wonder Full
Not ranked
Basis
BenchAlign v5.8 lane · 11 vs 2 public rows
Reading
Not comparable

Reasoning

Not comparable
GPT-6 Astra
93.3
#1/29
Muse Wonder Full
83.4
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-6 Astra
87.3
Unranked · 3 rankable rows
Muse Wonder Full
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-6 Astra
Not ranked
Muse Wonder Full
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GPT-6 Astra
Not ranked
Muse Wonder Full
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-6 Astra
85.5
Unranked · 1 rankable row
Muse Wonder Full
Not ranked
Basis
Provisional lane · 1 vs 0 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

GPT-6 Astra
$0.035
Fits in one request
Muse Wonder Full
API rate not published
Fits in one request

Muse Wonder Full has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-6 Astra
$0.65
Fits in one request
Muse Wonder Full
API rate not published
Fits in one request

Muse Wonder Full has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-6 Astra
$0.9
Fits in one request
Muse Wonder Full
API rate not published
Fits in one request
Cached-input rate unavailable

Muse Wonder Full has no comparable published API token rate.

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.

Provider availability

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

Muse Wonder Full

Not sourced

Reasoning profile

GPT-6 Astra

Reasoning

Muse Wonder Full

Reasoning

Weight access

GPT-6 Astra

Proprietary

Muse Wonder Full

Proprietary

License

GPT-6 Astra

Proprietary

Muse Wonder Full

Proprietary

Release date

GPT-6 Astra

2026-09-03

Muse Wonder Full

2026-10-12

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, 83.26 versus 73.81. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
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, GPT-6 Astra or Muse Wonder Full?

GPT-6 Astra has the higher public point estimate, 83.26 versus 73.81. Their conditional score ranges 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, GPT-6 Astra or Muse Wonder Full?

GPT-6 Astra has the higher public coding point estimate, 75.7 to 74, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, GPT-6 Astra or Muse Wonder Full?

Muse Wonder Full is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-6 Astra or Muse Wonder Full?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-6 Astra or Muse Wonder Full?

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 evidence39 rows

Agentic

  • BrowseComp

    GPT-6 Astra91.5%
    Source
    Muse Wonder Full—

    Not directly comparable

  • OSWorld 2.0

    GPT-6 Astra72.6%
    Source
    Muse Wonder Full—

    Not directly comparable

  • Terminal-Bench 4.0

    GPT-6 Astra57.90%
    Source
    Muse Wonder Full—

    Not directly comparable

  • Terminal-Bench-Science 0.1

    GPT-6 Astra64.6%
    Source
    Muse Wonder Full—

    Not directly comparable

  • ExploitGym

    GPT-6 Astra42.4%
    Source
    Muse Wonder Full—

    Not directly comparable

  • Agents' Last Exam

    GPT-6 Astra59.3%
    Source
    Muse Wonder Full—

    Not directly comparable

  • AutomationBench

    GPT-6 Astra41.4%
    Source
    Muse Wonder Full—

    Not directly comparable

  • HLE w/ tools

    GPT-6 Astra57.2%
    Source
    Muse Wonder Full—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GPT-6 Astra87.3%
    Source
    Muse Wonder Full—

    Not directly comparable

  • ApprenticeBench

    GPT-6 Astra68%
    Source
    Muse Wonder Full—

    Not directly comparable

  • CWE-bench v1

    GPT-6 Astra68.0%
    Source
    Muse Wonder Full—

    Not directly comparable

  • JobBench

    GPT-6 Astra—
    Muse Wonder Full75.3%
    Source

    Not directly comparable

  • Finance Agent v2

    GPT-6 Astra—
    Muse Wonder Full59.1%
    Source

    Not directly comparable

Coding

  • DeepSWE

    GPT-6 Astra74.1%
    Source
    Muse Wonder Full79.0%
    Source

    Muse Wonder Full leads this result

  • FrontierCode 1.1 Main

    GPT-6 Astra53.3%
    Source
    Muse Wonder Full—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-6 Astra64.5%
    Source
    Muse Wonder Full—

    Not directly comparable

  • FrontierSWE v2

    GPT-6 Astra65.5%
    Source
    Muse Wonder Full—

    Not directly comparable

  • PostTrainBench v1.1

    GPT-6 Astra44.3%
    Source
    Muse Wonder Full—

    Not directly comparable

  • Bug Hunt Bench

    GPT-6 Astra45.0 fixes
    Source
    Muse Wonder Full—

    Not directly comparable

  • ProgramBench

    GPT-6 Astra—
    Muse Wonder Full59.0%
    Source

    Not directly comparable

  • sweMarathon

    GPT-6 Astra—
    Muse Wonder Full57.5%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-1

    GPT-6 Astra98.50%
    Source
    Muse Wonder Full—

    Not directly comparable

  • ARC-AGI-2

    GPT-6 Astra95%
    Source
    Muse Wonder Full—

    Not directly comparable

  • ARC-AGI-3

    GPT-6 Astra62.7%
    Source
    Muse Wonder Full—

    Not directly comparable

  • GeneBench-Pro

    GPT-6 Astra37.8%
    Source
    Muse Wonder Full—

    Not directly comparable

  • MRCR v2 256K-512K

    GPT-6 Astra100.0%
    Source
    Muse Wonder Full—

    Not directly comparable

  • MRCR v2 512K-1M

    GPT-6 Astra96.3%
    Source
    Muse Wonder Full—

    Not directly comparable

  • GraphWalks BFS 256K–1M

    GPT-6 Astra71.8%
    Source
    Muse Wonder Full—

    Not directly comparable

Multimodal

  • ScreenSpot Pro

    GPT-6 Astra92.7%
    Source
    Muse Wonder Full—

    Not directly comparable

  • BenchCAD Vision2Code (tools)

    GPT-6 Astra0.959
    Source
    Muse Wonder Full—

    Not directly comparable

Knowledge

  • GPQA

    GPT-6 Astra96%
    Source
    Muse Wonder Full—

    Not directly comparable

  • GPQA-D

    GPT-6 Astra96.0%
    Source
    Muse Wonder Full—

    Not directly comparable

  • HealthBench (raw)

    GPT-6 Astra56.9%
    Source
    Muse Wonder Full—

    Not directly comparable

  • HealthBench (length-adjusted)

    GPT-6 Astra58.3%
    Source
    Muse Wonder Full—

    Not directly comparable

  • HealthBench Professional

    GPT-6 Astra64.7%
    Source
    Muse Wonder Full62.3%
    Source

    GPT-6 Astra leads this result

  • HealthBench Professional (raw)

    GPT-6 Astra68.2%
    Source
    Muse Wonder Full—

    Not directly comparable

  • HealthBench Hard

    GPT-6 Astra36.6%
    Source
    Muse Wonder Full—

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    GPT-6 Astra8.5%
    Source
    Muse Wonder Full—

    Not directly comparable

Math

  • FrontierMath v2 (Tier 4)

    GPT-6 Astra97.600%
    Source
    Muse Wonder Full—

    Not directly comparable

39 public results · 2 shared

Watch GPT-6 Astra vs Muse Wonder Full

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 5,500+ readers.

Last updated October 12, 2026