Meta’s release of Muse Wonder and Muse Wonder Full on October 12, 2026, creates a new choice for developers evaluating AI agents and coding models. The important question is not simply which model has the biggest headline score. It is which system offers the right combination of task performance, API cost, access, and reliability for the work you need it to do.

This comparison looks at four options: Meta Muse Wonder, Meta Muse Wonder Full, Anthropic Claude Opus 5.5, and OpenAI GPT-6 Astra. It uses published model-card results and official pricing information, while separating developer claims from independent evaluation. It is not a hands-on test: no new benchmark runs were conducted for this article.

Quick answer

  • For an API-based evaluation at a published token price: Muse Wonder and Claude Opus 5.5 are listed at $4 per million input tokens and $20 per million output tokens. GPT-6 Astra is listed at $10 input and $50 output per million tokens.
  • For long-running coding tasks: Meta reports strong Muse Wonder Full results on DeepSWE and SWE-Marathon, but the model is currently accessed through a Muse Code subscription rather than a publicly priced token API.
  • For a broad performance comparison: no single benchmark establishes a universal winner. The reported results vary by task, and the source and method behind each score matter.
  • For a purchasing decision: compare total cost to complete your own tasks, not just the token price or one leaderboard position.

Prices and access terms can change. Check the official product pages linked below before committing to a production workload.

What changed?

Meta announced Muse Wonder and Muse Wonder Full on October 12. The company positions them for coding, long-running agents, computer use, and professional workflows. Muse Wonder is available through the Meta Model API and Muse Code; Muse Wonder Full is available to subscribers of Muse Code. Meta says broader integration into the Muse assistant will follow, beginning with subscribers.

The launch matters because it adds another frontier model family to a market where developers increasingly need models to execute multi-step tasks, recover from errors, and use tools—not merely generate a plausible answer. It also introduces a practical access distinction: the two Meta models are not offered through the same route.

The release does not settle which model is best for every developer. Model-card scores reflect selected evaluations under specified conditions. Real projects differ in codebase size, tools, context, success criteria, and how much human review is required.

Benchmark results: what can we compare fairly?

Meta’s model card reports results across coding and agent benchmarks. VentureBeat’s coverage of the release reproduces a selection of those figures and identifies the table as sourced from Meta’s model card. The following values are therefore best treated as published model-card results, not as a new independent head-to-head test conducted by this publication.

Benchmark Muse Wonder Muse Wonder Full Claude Opus 5.5 GPT-6 Astra
Artificial Analysis Intelligence Index 52 56 58 53
DeepSWE 1.1 78.8 79.0 74.2 74.1
SWE-Marathon 53.1 57.5 46.3 42.5

Source: figures reported in Meta’s Muse Wonder model card and reproduced in VentureBeat’s coverage. Scores are benchmark-specific; higher is better within a given benchmark. Do not compare scores from different benchmark columns as though they shared one scale.

These results suggest different strengths rather than a single universal ordering. Claude Opus 5.5 has the highest score among these four in the Intelligence Index shown in the table. Muse Wonder Full leads the listed DeepSWE and SWE-Marathon results. Those coding benchmarks are especially relevant to sustained software-engineering tasks, but they do not prove that Muse Wonder Full will be the best choice for every repository, programming language, or workflow.

There are also limits to what can be concluded from the table:

  • Different tests measure different things. A composite intelligence index is not interchangeable with a software-engineering benchmark.
  • The evaluation setup matters. Reasoning effort, tool access, harnesses, task selection, and success criteria can change results.
  • A score is not a guarantee. A model can perform well on a benchmark and still make mistakes in your codebase or fail to follow a project-specific requirement.
  • Source attribution matters. Meta’s model card is a primary source for the company’s reported figures. Coverage that reproduces those figures is useful context, but it does not turn every number into an independently replicated result.

For that reason, treat the table as a shortlist generator. If your work is mainly long-running coding, inspect the coding results and test those models on your own repository. If your work is research, document analysis, or general professional assistance, the coding leaderboard alone is not enough.

API pricing: the listed cost per token

Official product pages list the following standard input and output prices:

Model Input per 1M tokens Output per 1M tokens Access notes
Meta Muse Wonder $4 $20 Meta Model API and Muse Code
Claude Opus 5.5 $4 $20 Claude Platform and supported cloud platforms
GPT-6 Astra $10 $50 OpenAI API and announced supported platforms
Muse Wonder Full No public token price listed No public token price listed Included with eligible Muse Code subscription plans

These are token rates, not guaranteed prices for completing a task. They exclude any separate tool charges and do not account for retries, caching patterns, context length, or the number of model calls required to reach a correct result. Muse Wonder’s official pricing page also lists a lower cached-input rate; check the pricing page for current conditions.

On the published standard rates, Muse Wonder and Claude Opus 5.5 have the same base input and output prices. GPT-6 Astra’s listed standard rates are higher. That does not automatically make the less expensive models better value: if a model needs more attempts, produces more tokens, or requires more human correction, its total cost can be higher.

Muse Wonder Full needs a different comparison. Since the public model page presents it as subscription access through Muse Code rather than a separately priced token API, a direct per-token comparison would be misleading. For a team already paying for Muse Code, the subscription may be the relevant cost. For a team that needs programmatic API access and metered usage, the availability difference may be more important than the benchmark score.

A simple way to estimate real cost

Before choosing a model, estimate the actual workload:

  1. Define a representative task, such as fixing a bug across multiple files or implementing a small feature with tests.
  2. Record input tokens, output tokens, cached tokens, tool calls, and retries.
  3. Decide in advance what counts as success and have a reviewer verify the result.
  4. Repeat the task several times rather than relying on one lucky run.
  5. Calculate cost per successful, reviewed task, including human correction time if that affects your budget.

This is more useful than multiplying a headline score by a token price. A model with a higher per-token rate may still be economical if it reliably completes the task in fewer attempts. Conversely, a cheap call is poor value if the output is unusable.

Access and product fit

Meta Muse Wonder

Muse Wonder is the clearest Meta option to evaluate when you want API access and agent-oriented capabilities. Meta highlights coding, computer use, long-running tasks, Active Memory, and Active Focus. The company describes Active Memory as carrying relevant context across conversations and Active Focus as continuing to work through blockers, trying alternatives, and recovering from errors.

These descriptions establish what Meta intends the model to do; they do not independently establish how reliably it will do so in every environment. Developers should check API availability, rate limits, tool support, data-handling terms, and current pricing before designing around it.

Meta Muse Wonder Full

Wonder Full is positioned as the more capable model for demanding workflows, and its published results are strong on the two coding benchmarks shown above. Its access model is different, however: Meta currently lists it as available through Muse Code subscription plans, not with a public per-token API rate in the pricing table.

That makes it a natural candidate for developers whose workflow fits Muse Code and who want to test long-running coding tasks. It is less straightforward to compare with API-metered models on cost alone. Confirm the subscription plan, usage limits, and availability that apply to your account before making a decision.

Claude Opus 5.5

Anthropic positions Claude Opus 5.5 for complex coding, long-running agents, and professional work. Its official page lists API access and pricing of $4 per million input tokens and $20 per million output tokens, with separate terms for fast mode and cached tokens.

The benchmark table above shows Opus 5.5 ahead of the other three on the reported Artificial Analysis Intelligence Index, while the Meta models lead the listed DeepSWE and SWE-Marathon results. This is a useful illustration of why the intended task matters. Do not infer that the Intelligence Index score settles which model will be most productive in your own codebase.

OpenAI GPT-6 Astra

OpenAI describes GPT-6 Astra as a model for software engineering, browsing, computer use, science, and professional work. Its official announcement lists access through ChatGPT plans and the API, alongside supported cloud offerings. Standard API pricing is $10 per million input tokens and $50 per million output tokens, with separate pricing for fast mode and cached tokens.

In the table, GPT-6 Astra’s reported coding scores trail the Meta models on DeepSWE and SWE-Marathon, while its Intelligence Index score is between Muse Wonder and Claude Opus 5.5. Those results do not make it a poor choice by default: access arrangements, tool integrations, safety requirements, latency, and the performance of the complete workflow may affect the decision.

Which model should you choose?

Choose based on the workflow, not the brand.

  • You need an API and want to test agentic coding: start with Muse Wonder and Claude Opus 5.5. Their listed standard token prices are the same, making task success, latency, tool support, and reliability key variables.
  • Your work involves long-running software-engineering tasks: include Muse Wonder Full in a controlled evaluation. Its reported DeepSWE and SWE-Marathon results are strong, but account for Muse Code’s subscription-based access.
  • You already use OpenAI’s API or tooling: include GPT-6 Astra in the test set. Compare the value of its integration and task completion against its higher listed standard token rates.
  • You need professional research or document analysis rather than coding: do not choose based on DeepSWE or SWE-Marathon alone. Build a small evaluation set from the documents and questions your users actually have.
  • You have strict cost limits: measure cost per successful task. Token prices are only one part of the total.

For a fair comparison, use the same requirements, tools where possible, and acceptance tests. Keep model versions and settings fixed, and document any differences that cannot be standardized. For tasks that involve external actions or sensitive information, measure whether the model respects permissions and asks for approval at the right time. A successful benchmark run is not a substitute for a safe deployment design.

Limitations and what to verify before buying

This article does not claim to have independently rerun the benchmarks or performed hands-on tests. The benchmark figures are reproduced from published reporting of Meta’s model card, and vendor product pages are the source for current feature, access, and pricing claims. Vendor benchmark claims should be distinguished from independent third-party evaluations, and comparisons are only meaningful when their test methods are understood.

Before using these results to make a purchasing decision, verify:

  • The exact model ID and version available to your account.
  • Whether the feature or model is available in your region and plan.
  • Current input, output, cached-token, batch, and fast-mode prices.
  • Whether the benchmark uses the same tools, reasoning settings, and harness as your intended workflow.
  • Total cost and success rate across several representative tasks.
  • Data retention, privacy, permissions, and safety controls required for your use case.

Bottom line

Muse Wonder and Muse Wonder Full make Meta a more serious option for developers evaluating agentic coding. The published results show Wonder Full performing strongly on the listed software-engineering benchmarks, while Claude Opus 5.5 leads the selected Intelligence Index comparison. GPT-6 Astra remains a relevant candidate, especially where OpenAI’s access and tool ecosystem fit the workflow.

The clearest pricing distinction is that Muse Wonder and Claude Opus 5.5 are listed at $4 per million input tokens and $20 per million output tokens, while GPT-6 Astra is listed at $10 and $50. Wonder Full’s subscription-based access makes a direct token-price comparison inappropriate.

There is no defensible universal winner from these numbers alone. Select two or three candidates, test them on the same real tasks, and compare verified outcomes, total cost, and the amount of human repair required.

Sources

Information checked on October 13, 2026. Product access, pricing, and benchmark leaderboards can change; verify the linked official pages before making a purchasing decision.