meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5

meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts:

• 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work

• trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back

• computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step

• first public api from meta: the meta model api is now in preview

• benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2

our test – 3 prompts, single-file html, three.js, fully procedural, no assets:

1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior
2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows
3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation

we ran the test on @aimlapi platform

results:

- cost
#1 muse spark 1.1 – $0.20
#2 grok 4.5 – $0.51
#3 gpt 5.6 sol – $1.93
#4 fable 5 – ~$5.20

- output tokens
#1 muse spark 1.1 – 41,868
#2 gpt 5.6 sol – 49,139
#3 grok 4.5 – 64,954
#4 fable 5 – 81,849

- lines of code
#1 muse spark 1.1 – 1,799
#2 gpt 5.6 sol – 2,377
#3 fable 5 – 3,088
#4 grok 4.5 – 4,216

observations:

• muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price

• only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it

• the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one

muse spark's code quality – reviewed by fable 5:

upsides:

1. all three files run
2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally
3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad
4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers

downsides:

1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model
2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second
3. the siheyuan registers two separate keydown listeners, one containing an empty if-block
4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation
5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere

pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust