TL;DR: Meta’s Muse Wonder and OpenAI’s GPT-6.1 Sol are advanced reasoning models for coding, professional workflows, and AI agents. Both offer approximately one million tokens of context, but differ in model design, output speed, and usage costs.
Artificial Analysis reports an Intelligence Index score of 52 for both models in its Max comparison, checked on October 15, 2026. Muse Wonder leads on selected business and automation evaluations; GPT-6.1 Sol leads on selected terminal-coding and long-context tests. These are independent third-party results, not tests conducted for this article.
According to the dated pricing sources cited in this article, the listed base rates are $2/$10 per million input/output tokens for GPT-6.1 Sol and $4/$20 for Muse Wonder. These figures should be checked against the official provider documentation before production use. This makes GPT-6.1 Sol’s base rates 50% lower, but its long-context surcharges change that comparison. Choose using measured task success, latency and total billable usage, rather than token prices alone.
Key Takeaways
- Coding fit: GPT-6.1 Sol leads on the cited terminal-coding test; Muse Wonder leads on scientific programming. Match the benchmark to your workload.
- Agent fit: Evaluate memory continuity, recovery from tool failures and total completion time together.
- Context fit: A large context window is not persistent memory. Test retrieval accuracy on your documents and verify what the chosen API preserves across sessions.
- Deployment fit: Confirm route availability and reasoning controls; budget for cache writes, tools and retries.
Muse Wonder vs GPT-6.1 Sol: Overview and Specifications
At a Glance
Use this overview to distinguish each model’s positioning and access route. Detailed specifications, benchmark results and pricing appear in the relevant sections below.
| Decision factor | Muse Wonder | GPT-6.1 Sol |
|---|---|---|
| Starting workload | Persistent agents, scientific programming and business automation | Cost-sensitive reasoning and terminal-focused coding |
| Distinctive control | Active Memory and Active Focus | Five configurable reasoning-effort levels |
| Provider access | Meta Model API preview; Full is a separate offering | OpenAI API; use Responses for tool calling |
Muse Wonder: Persistent Agent Workflows
Meta introduced Muse Wonder on October 12, 2026. It targets complex coding, long-running agents, computer use and multimodal reasoning. Meta describes Active Memory as contextual continuity and Active Focus as sustained problem-solving. Developers can explore Muse Wonder API in CometAPI, while checking the gateway’s current access and supported parameters.
Do not confuse standard Muse Wonder API performance with Muse Wonder Full, a separately offered higher-capability variant. Claims for the Full variant should not be transferred to the standard API model.
GPT-6.1 Sol: Cost-Conscious Reasoning
GPT-6.1 Sol targets complex coding, computer use and professional work. OpenAI documents a 1,050,000-token context window, a maximum output of 128,000 tokens, and low, medium, high, xhigh and max reasoning efforts. Developers can access GPT-6.1 Sol API in CometAPI. For the native OpenAI API, tool calling requires Responses; Chat Completions is supported without tool calling.
Architecture, Reasoning Controls and Specifications
Muse Wonder emphasizes agent continuity and memory-oriented capabilities; GPT-6.1 Sol gives developers explicit reasoning-effort choices and a familiar API orchestration approach. Neither architectural emphasis alone proves better application-level reliability.
| Specification | Muse Wonder | GPT-6.1 Sol |
|---|---|---|
| Developer | Meta | OpenAI |
| API availability | Meta Model API preview | Documented OpenAI API model |
| Main emphasis | Persistent agents, coding, multimodal reasoning | Coding, reasoning, computer-use workflows |
| Context window | 1,048,576 tokens | 1,050,000 tokens |
| Maximum output | Not specified in the cited overview; consult the selected API surface | 128,000 tokens |
| Text and image input | Yes | Yes |
| Audio/video input | Video described by Meta; audio support requires release-surface confirmation | Not native model input |
| Text output | Yes | Yes |
| Tool use | Yes | Yes |
| Memory orientation | Active Memory / Active Focus | Typically application-managed |
| Reasoning control | Reasoning effort is configurable; supported values depend on API surface | Low, medium, high, xhigh, max |
| Official input / 1M tokens | $4.00 | $2.00 |
| Official output / 1M tokens | $20.00 | $10.00 |
Meta’s API documentation specifies 1,048,576 context tokens and preview availability for Muse Wonder. Product features such as Active Memory are not disclosed neural-architecture specification. The cited sources do not provide enough comparable layer, parameter-count or expert-routing details for an architecture-level verdict.
Comparison result: Both support approximately one million context tokens and tool-enabled workflows. Muse Wonder’s memory-oriented positioning and GPT-6.1 Sol’s reasoning controls describe product behavior and configuration, rather than proving a model-architecture advantage.
Muse Wonder vs GPT-6.1 Sol: Performance
Published Benchmarks and Evaluation Conditions
This table reproduces the Artificial Analysis Max comparison checked on October 15, 2026, using Intelligence Index v4.3.2. The evaluator, rather than this article, ran the tests. Its published methodology describes endpoint-based evaluation; these figures are not a same-hardware experiment or a CometAPI speed test. Per-run hardware, region, sampling settings and complete harness configuration are not reproduced on the comparison page, so results should not be treated as a fully controlled deployment forecast.
| Category | Evaluation / capability | Muse Wonder | GPT-6.1 Sol | Result |
|---|---|---|---|---|
| Overall intelligence | Intelligence Index | 52 | 52 | Tie |
| Professional work | AA-Briefcase v1.1 | 1,659 | 1,558 | Muse Wonder |
| Professional work | GDPval-AA v2.1 | 1,770 | 1,594 | Muse Wonder |
| Automation | AutomationBench-AA | 68% | 65% | Muse Wonder |
| Terminal coding | Terminal-Bench 4.0 | 52% | 56% | GPT-6.1 Sol |
| Scientific coding | SciCode | 60% | 54% | Muse Wonder |
| General reasoning | Humanity’s Last Exam | 50% | 53% | GPT-6.1 Sol |
| Document tasks | GDP.pdf | 31% | 31% | Tie |
| Scientific reasoning | CritPt | 28% | 32% | GPT-6.1 Sol |
| Long-context reasoning | AA-LCR v1.1 | 81% | 83% | GPT-6.1 Sol |
Muse Wonder scores higher on AA-Briefcase and GDPval-AA, indicating an advantage in certain professional and business tasks. However, actual performance depends on the specific workload and deployment environment.
Coding and Software Engineering
Coding Performance
GPT-6.1 Sol leads on Terminal-Bench 4.0, a relevant signal for command-line troubleshooting and coding agents. Muse Wonder leads on SciCode, which emphasizes scientific programming. Neither benchmark alone establishes a universal winner for repository-scale engineering.
Long-Running Agent Workflows
For long-running agents, Muse Wonder emphasizes Active Memory and Active Focus, while GPT-6.1 Sol exposes reasoning settings within application-managed tool orchestration. Both support multi-file engineering workflows, but the cited comparison does not establish a winner for that capability. Test representative repository tasks and compare completion quality, retries, recovery and total spending.
Latency, Throughput and Reliability
The dated comparison reports approximately 131 versus 48 output tokens per second for Muse Wonder and GPT-6.1 Sol respectively, or roughly 2.7× the throughput. These are third-party endpoint measurements under the listed Max configurations, not a guarantee for other providers, traffic levels or reasoning settings.
Output throughput is not the same as end-to-end latency. Initial reasoning time, tool calls, retries, and the amount of generated content can outweigh differences in tokens per second. Measure the time required to finish a useful task.
Multimodal Input and Long-Context Reasoning
| Capability | Muse Wonder | GPT-6.1 Sol |
|---|---|---|
| Text and document analysis | Yes | Yes |
| Image understanding | Yes | Yes |
| Video / audio | Video described by Meta; confirm audio and gateway exposure separately | No native support in cited specification |
| Context window | 1,048,576 tokens | 1.05M tokens |
| Persistent memory | Active Memory design | App-managed memory |
On the independent AA-LCR v1.1 long-context reasoning benchmark, GPT-6.1 Sol scores 83%, narrowly ahead of Muse Wonder at 81%. Muse Wonder instead differentiates itself through a memory-oriented agent design rather than context size alone. Neither context capacity nor memory branding guarantees accurate retrieval or cross-session persistence in every API deployment.
Muse Wonder vs GPT-6.1 Sol: Cost
Consolidated Pricing and Request Example
| Pricing basis | Charge / condition | Muse Wonder | GPT-6.1 Sol |
|---|---|---|---|
| Official standard | Input / 1M tokens | $4.00 | $2.00 |
| Official standard | Cached input / 1M tokens | $0.20 | $0.10 |
| Official standard | Output / 1M tokens | $20.00 | $10.00 |
| Official long context | Input / cached input / output per 1M | Not separately specified in the source article | $4.00 / $0.20 / $15.00 above 272,000 input tokens |
| Official cache writes | Write / 1M tokens | Not specified in the source article | $2.50 base; $5.00 above the long-context threshold |
| Official request example | 100,000 uncached input + 20,000 billable output tokens | $0.40 input + $0.40 output = $0.80 | $0.20 input + $0.20 output = $0.40 (50% lower) |
| CometAPI indicative | Input / 1M tokens | $3.20 | $1.60 |
| CometAPI indicative | Cached input / 1M tokens | $0.16 | $0.08 |
| CometAPI indicative | Output / 1M tokens | $16.00 | $8.00 |
| CometAPI long context | Input / output per 1M | Not separately specified in the source article | $3.20 / $12.00; separate cache-write charges apply |
| Model identifier | Check the intended route | Gateway example: muse-wonder; native Meta: muse-wonder-1.0 | gpt-6.1-sol |
These are standard base rates checked on October 15, 2026. OpenAI applies long-context pricing above 272,000 input tokens: input and cache rates double, and output rates increase by 50%, for the full request. Thus GPT-6.1 Sol becomes $4 input, $0.20 cached input and $15 output per million tokens in that tier. Cache writes cost $2.50/M at the base tier and $5/M above the threshold. Fast, Ultrafast, Batch, Flex and regional processing have separate adjustments. Do not extend the base-rate 50% saving to every workload.
This simple token-cost example excludes tool surcharges, retries, cache-writing charges, and any applicable long-context or routing adjustments.
CometAPI’s displayed base estimates are $3.20/$16 for Muse Wonder and $1.60/$8 for GPT-6.1 Sol per million input/output tokens. The GPT-6.1 Sol pricing table also displays $3.20/$12 for long-context input/output and separate cache-write charges. Treat gateway rates as a dated catalog snapshot. Muse Wonder’s page uses muse-wonder-1.0 in its specification and muse-wonder in a sample; resolve the accepted identifier with the active gateway before integration. Catalog presence does not establish successful access for a particular account.
Cost per Accepted Task
| Metric | Muse Wonder (Max) | GPT-6.1 Sol (Max) |
|---|---|---|
| Official input / 1M | $4.00 | $2.00 |
| Official output / 1M | $20.00 | $10.00 |
| Reported average evaluated task cost | $4.52 | $0.72 |
| Intelligence Index | 52 | 52 |
The dated benchmark snapshot lists $4.52 versus $0.72 per evaluated task, approximately a 6.3× difference. Artificial Analysis defines this as a weighted average across Intelligence Index workloads using token consumption and applicable pricing. It is not an observed universal production multiplier or an estimate of CometAPI billing.
The correct commercial metric is cost per successful task. A model with a higher token price can still be efficient if it uses fewer tool calls or succeeds more often. Measure all retries, tokens, tools, and human review costs.
Comparison result: GPT-6.1 Sol has lower listed base token rates, while its long-context tier narrows that advantage. Production economics depend on cache behavior, billable reasoning, retries, tools and accepted outcomes, so benchmark task cost and gateway token prices should be reported separately.
Muse Wonder vs GPT-6.1 Sol: Comparison and Selection
Muse Wonder Selection Criteria
Evaluate Muse Wonder for persistent research agents, scientific programming, business automation or throughput-sensitive generation. Its cited SciCode and AutomationBench results, memory-oriented capabilities and output throughput justify a trial; select it only if those advantages improve accepted-task quality, completion time or total cost on your workload.
GPT-6.1 Sol Selection Criteria
Start with GPT-6.1 Sol for cost-sensitive chatbots, frequent or high-volume inference, terminal debugging and workflows that need explicit reasoning controls. For enterprise documents, compare both models on representative files and acceptance criteria. Confirm effective cost after long-context adjustments, reasoning tokens, tools and retries.
Hybrid Deployment
A routing approach can reserve Muse Wonder for selected persistent-agent scenarios while using GPT-6.1 Sol for everyday reasoning, document work, and frequent calls. Set routing rules based on task outcomes rather than rankings alone.
Production Evaluation Checklist
A fair comparison uses equivalent task definitions, comparable inputs, clearly documented model configurations, and the same acceptance criteria. Start by checking route availability and valid parameters, then run representative prompts and workflows through both models.
- Build a task set that includes coding, reasoning, document understanding, and agent tool use.
- Keep instructions, tools, retrieval inputs, and success criteria as comparable as possible.
- Test GPT-6.1 Sol reasoning settings independently instead of equating them with Muse Wonder settings.
- Record accuracy, end-to-end latency, tokens, tool calls, retries, and user-relevant failure modes.
- Compute total cost per successful task before selecting a default or routing strategy.
Define acceptance criteria before testing, then report correctness, completion without human rescue, median and tail latency, total billed tokens, tool calls and corrective retries. Calculate model-and-tool spend per accepted task; report review labor separately or convert it using an explicit hourly rate. A candidate should meet quality and reliability thresholds before cost determines the final selection.
Where Can You Access Muse Wonder and GPT-6.1 Sol?
Official Provider Access
Standard Muse Wonder is available through Meta Model API in preview. Muse Wonder Full is a separate Muse Code subscription offering and should not be treated as a drop-in standard API upgrade. GPT-6.1 Sol is documented for OpenAI API access; use Responses for tool calling. Confirm account permissions, usage limits and supported request features before committing to a provider route.
CometAPI Access
A shared gateway can simplify authentication and billing, but it does not make model identifiers, reasoning parameters, tool formats or stored state interchangeable. Keep application-side task state and validate each route’s behavior independently.
Before integration, inspect the current identifiers and account access for Muse Wonder API in CometAPI and GPT-6.1 Sol API in CometAPI. Send a small request through the intended endpoint, verify reasoning and tool support, inspect reported token usage, and then run the workload comparison.
Conclusion
The two models have comparable overall measured intelligence but different strengths. Muse Wonder is notable for agent-centered design, strong professional benchmarks, and high evaluated output throughput. GPT-6.1 Sol provides lower standard token prices, explicit reasoning controls, and strong terminal-coding and long-context results.
GPT-6.1 Sol is a practical default for cost-conscious general-purpose applications. Muse Wonder warrants targeted evaluation where persistence, automation, or throughput affects real business value. The final choice should be driven by successful completed work rather than one benchmark score.
FAQ
How should teams test Muse Wonder and GPT-6.1 Sol before switching production traffic?
Use a held-out task set that includes ordinary requests and expensive failure cases, such as broken tool arguments, incomplete repository changes and unsupported document claims. Fix the acceptance rubric before running either model, blind the review where possible, and repeat tasks to expose run-to-run variation. Set pass-rate, latency and spending limits before a small canary rollout, with a rollback path if those limits are missed.
How can reasoning tokens affect a Muse Wonder vs GPT-6.1 Sol cost comparison?
A short final answer can follow a much larger reasoning trace, so visible answer length is an unreliable bill estimate. Collect the provider’s input, cached, reasoning and output usage fields where available, reconcile them against actual charges, and include repeated attempts. If the selected endpoint does not expose a field separately, report that measurement limitation rather than assuming the usage is zero.
Can a Muse Wonder or GPT-6.1 Sol model update invalidate an earlier evaluation?
Yes. A model alias, gateway route, prompt template or evaluation harness can change while the article title remains the same. Save the request date, returned model identifier, endpoint, reasoning setting, tool definitions and test-set version with each run. Repeat the acceptance suite after a material change and retain previous results so the team can distinguish model movement from changes in its own application.
What should a fallback between Muse Wonder and GPT-6.1 Sol preserve?
Preserve the user’s goal, authoritative retrieved evidence, completed tool actions and the remaining work. Do not blindly replay side-effecting operations after a timeout; check whether the earlier action completed first. Translate model-specific request parameters explicitly and keep an external execution log, because a shared API format does not guarantee identical state, tool or reasoning semantics.