Muse Wonder Full isn't the model in the API
Meta's best Muse Wonder results belong to Full, available through Muse Code subscriptions. I separate model access, benchmark settings and the API bill.
benchmarks
Every article tagged benchmarks, newest first.
Gemini 4 Argon: strong scores, uneven coding results
Google's Argon leads DeepSWE but trails on other coding tests. I compare the launch benchmarks, million-token output limit and restricted rollout.
Claude Sonnet 5.5: maximum effort can cost more than Opus
Sonnet 5.5 keeps its token prices and generates output faster. Independent tests show why maximum effort can make the cheaper model cost more per task.
GPT-6.1 Sol gets close to Astra at a lower task cost
Independent tests put Sol 6.1 close to Astra at a much lower cost per task. I compare the evidence, API rates and the first Reddit reactions.
GPT-6 Sol and Luna: the price cut is the upgrade
GPT-6 Sol and Luna cut API prices while independent benchmarks show smaller gains. I compare the scores, ChatGPT access, and mixed first reports on Reddit.
Opus 5.5 improves coding. Task cost is less clear
Opus 5.5 gains ground in independent coding tests and cuts token prices. I compare the task costs, effort settings, and first Reddit reports on daily use.
GPT-6 Astra costs 2.5x Sol per token. It talks like a person
OpenAI's GPT-6 Astra is metered at 2.5 times Sol's rate and feels more human in conversation. I checked the limits, the benchmarks, and the first reactions.
Muse Spark 1.3 sat above GPT-6 for two days. Here's why
Muse Spark 1.3 scored 62 on Artificial Analysis, above GPT-6 Astra, then fell to ninth in a rescore. I traced where the score came from and what it costs.
Fable 5.1 beats Fable 5. Opus is harder to replace
Fable 5.1 leads its predecessor on long agent tasks, but Opus 5 stays close at half the base token price. I checked the benchmarks and first reports.
DeepSeek V4 Flash lost all 9 benchmarks to Opus 4.8
V4 Flash costs $0.14 in and $0.28 out per million tokens. I checked whether its 97 to 99 percent discount makes DeepSeek's benchmark losses worth it.
Why similar AI benchmark scores can hide different models
DeepSWE puts Luna Max 2.2 points behind Sol High at roughly one-sixth the attempt cost. I explain why the models can still feel far apart in repository work.
GPT-5.6 vs Claude 5: why the benchmarks disagree
I compared Sol, Terra, Opus 5 and Fable 5 across coding benchmarks. The winner changes with the task, effort setting, agent setup and budget.
Kimi K3 matches GPT-5.6. It takes 4.6 times as long
Kimi K3 ties GPT-5.6 medium but takes 4.6 times as long. GLM-5.2 is cheap per token yet costly per task. I checked where both models still win.
Opus 5 is a great employee but a frustrating boss
Opus 5 matches Fable 5 on benchmarks at half the token price, yet it can be painful to supervise. Here is where Fable still earns its place.