AI/ML API

Grok 4.5 crushed OpenAI & Meta in this test! Cost per run: GPT Sol $1.63 Grok 4.5 $2.47 Meta Muse Spark 1.1 $1.08 The three prompts: • Fruit Ninja – style slicing game • Angry Birds – style fort collapse • Crossy Road clone Grok nailed all three: clean physics, smooth playback, good visuals. GPT Sol did fine until Crossy Road, where it froze. Meta Muse Spark was cheapest and it showed: its Crossy Road lagged badly. The question isn't which model is cheapest, it's what a broken output costs you in reruns, wasted time, and things you can't ship. Cheap stops being cheap when it doesn't work.

@thehypedotnews Such an awesome stuff you can create with multiple models at hand

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 follow @thehypedotnews for 24/7 ai news, analysis and breakdowns

@the_great_hug0 @cryptoninjap2e Time to cook

@aimlapi where it was in 2024 when I was building 😭😭😭@cryptoninjap2e

Grok 4.5 crushed OpenAI & Meta in this test! Cost per run: GPT Sol $1.63 Grok 4.5 $2.47 Meta Muse Spark 1.1 $1.08 The three prompts: • Fruit Ninja – style slicing game • Angry Birds – style fort collapse • Crossy Road clone Grok nailed all three: clean physics, smooth playback, good visuals. GPT Sol did fine until Crossy Road, where it froze. Meta Muse Spark was cheapest and it showed: its Crossy Road lagged badly. The question isn't which model is cheapest, it's what a broken output costs you in reruns, wasted time, and things you can't ship. Cheap stops being cheap when it doesn't work.

@hypersonq It should be

@aimlapi Maybe muse spark is better in launching meta ads?

@ajs6888 You liked Grok the most ?

@aimlapi 成本也一起碾压了，有点真实

@Awesome_O_AI @Ronycoder that's why games are a good benchmark

@Ronycoder Benchmarking should include consistency, not just speed and price.

Grok 4.5 winning this AI/ML API comparison isn't what stood out to me. The bigger takeaway is how often people optimize for price while overlooking the cost of failed generations. A few extra cents per request is easy to justify if it saves hours of reruns, debugging, and shipping delays. That's the part of AI economics benchmark charts rarely capture.

@HeyAmit_ @Ronycoder absolutely

@Ronycoder This is a reminder that the cheapest model isn't always the smartest business decision.

@rakhul @testingcatalog we'll create doom with new gemini when it's out

@testingcatalog Game generation is underrated as a benchmark. It forces the model to hold state, handle collision logic, and write coherent loops, not just produce plausible-looking code. Curious how cost-per-playable-game shakes out between the three.

Grok 4.5 and Muse Spark 1.1 are now available on the AI/ML platform for testing. What a week; all 3 models in the comparison dropped just recently. There will be a lot to experiment with. AI/ML has tested the new models on prompts for playable mini-games, including a Fruit Ninja-style slicer and a Crossy Road clone. All three are live in the Playground and via API for side-by-side runs on output and cost.

@itsthedonhashim @testingcatalog give it a shot!

@testingcatalog @testingcatalog grok 4.5 dropped right when I was about to wrap up a project. guess i'll be testing all weekend now 😅

@0xSlyth @karankendre shocking

@karankendre grok actually performing well is wild

Grok 4.5 was the only model that actually worked across all three physics game tests. Prompts : > Fruit Ninja slicing game > Angry Birds fort collapse > Crossy Road clone Grok 4.5 produced clean, playable results every time GPT Sol froze on Crossy Road Meta Muse Spark (the cheapest) showed clear lag and lower quality Sometimes the slightly higher price tag saves you from much bigger costs downstream

@AGTPinsights @karankendre @grok are you great?

@karankendre This is what I want to see. Real-world performance on actual tasks beats benchmarks every time. Grok 4.5 shipping consistently across three different games tells me something.

@OKristinavl @atomic_chat_hq We'll wait

@aimlapi @atomic_chat_hq OpenAI & Meta: “We’ll be back.” 😅

@Awesome_O_AI and beautiful

@aimlapi AI model evaluation is becoming much more practical.

@GeniusPothead @DataChaz And that is absolutely great.

@DataChaz @aimlapi Competition is finally pushing AI pricing in the right direction for developers

THE PRICE GAP BETWEEN FRONTIER MODELS IS DEFINITELY GETTING WILDER a new @aimlapi test shows Fable 5 costing $9.94, while GPT-5.6 Terra delivers the same quality for just $1.24 🤯 Now that’s a HUGE drain on your margins. Staying locked into one provider has become a major financial risk. That's where AI/ML API comes in! They have simplified the infrastructure side by putting the entire AI landscape, from GPTs and Claude to Llama and DeepSeek, behind a single endpoint. It gives you the flex to pivot between 1000+ models based on real-time performance and pricing. Honestly, it feels like the only way to stay competitive right now. Benchmark breakdown here ↓

@DataChaz @0xSlyth That's what we do :)

@0xSlyth @aimlapi pretty crazy, right?! and I trust @aimlapi to provide unbiased results :)

@vikktorrrre sol > fable terra = opus

@aimlapi value for money?

GPT 5.6 Sol & Terra just made Fable 5's pricing look like a joke! We gave OpenAI's top models (Sol, Terra) and Anthropic's top models (Fable 5, Opus 4.8) the same 3 prompts: • Supernova boom • Meteor hitting a city • Solar system model The bill: Sol: $4.77 Terra: $1.24 Fable 5: $9.94 Opus 4.8: $2.46 Outputs came out surprisingly close. The prices didn't. Fable 5 cost unreasonably more than everything else, with Sol not far behind. Considering latest Fable 5 nerf, it's hard to see what you're paying for.

@n1ckstr3 It can still deliver - but no magic involved

@aimlapi fable is just a freaking slop today, damn 🤦🏻‍♂️

@notjazii @testingcatalog Not sure

@testingcatalog Fable > sol> terra > Opus 4.8

AI/ML API has added OpenAI's new GPT-5.6 models, Sol, Terra, and Luna, to its catalog of 1000+ models, all under the same API key. Users can run the new model in the Playground or via the API and test it against the rest of the stack without changing the setup. Who is winning? 👀