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Comments (86)

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  simonw
  Six effort levels (minimal, low, medium, high, xhigh, max), so here are six pelicans (actually five, "high" didn't return a complete SVG):https://tools.simonwillison.net/markdown-svg-renderer?url=ht...There's usually a reasonably strong correlation between thinking level and visual quality of the pelican. This is the first time I've seen that correlation appear to break - more reasoning tokens doesn't seem to produce an obviously better SVG.
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  Retro_Dev
  When they said the model was the "smallest" of their models yet, I was really hoping it would be open weights. I can't find any weights, how sad :(
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  kbrannigan
  This is starting to feel just like the early PC days. A bunch of Computer models coming out, all shapes and color, but eventually it settled around mouse keyboard, monitormost people, use the office suite: Word, Spreadsheet , presentations, database.I bet we're going to see the exact same thing with LLMs at specialized models instead of the everything models.One for lawyers, coders, Accountants, Designers, etc…
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  open-paren
  I believe this is what the “crouton alpha” stealth model washttps://openrouter.ai/stealth/crouton-alpha
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  minimaxir
  > It costs $0.10 per million input tokens, $0.004 per million cached input tokens and $0.50 per million output tokens,Notable cache cost of 4% input, compared to Luna/Haiku's 10% of input.
• Pragmata
  No weights? Shame
• syntaxing
  What does smallest mean? Even smaller than Muse Glimmer which I doubt? Though it is interesting to see US frontier labs trying to push models that outcompete deepseek flash 1017. It’s a good move from the Deepseek federal government ban. Both Inkling 2 small and this seems to try to fill that gap cost vs performance wise. But I like inkling 2 small more due to open weight even though it has a restrictive license.
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  ecshafer
  I would love it if Meta would release these models to use locally to build up some usage while they are lagging.
• slopnt
  I use haiku and luna for support on most my SWE tasks since they are cheap and fast. I find them enough for most things if you are patient, and the fact an even cheaper model for comparable intelligence is out there now is great news!
• segmondy
  What makes it small? Anyone know the size? I don't see it anywhere. Small in relation to their current line up? Anyways, last I tried Meta was llama4. I stepped aside not because of how bad llama4 got, but also because of Zuck's move. Anyone here using Meta? I don't know anyone in my circle using it, but I'm sure folks are using it.
• ImPostingOnHN
  Where exactly can we download this small model?How do we know it is small? How many bytes does it consume on-disk and in-memory?
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  ProblemExplorer
  Meta seems to have designed Ember to be a strong "all round" great value model - it dominates most of its class on costThe noncoding results on some reports grabbed my attention. Ember also looks like a particularly inexpensive option for some agent workflows.On Artificial Analysis, Ember max scored 36 on the Intelligence Index at ~$0.14 per task, compared with 35 at $8 for Grok 4.7 xhigh, and 28 at $0.15 for GPT-6 Luna max. Haiku 5.5 max also scores 36, at $0.41 per task. These are AA's measured costs across its index for those specific configurations.That puts Ember in roughly Grok's aggregate score range at about 1/55 of the measured cost, and ahead of Luna at a similar cost! [1]The Vals finance/legal results are also particularly interesting: comparing Ember with GPT-6.1 Terra, both evaluated at "max" reasoning:- Finance Agent v2.1: 63.83% vs 55.96%, at $0.06 vs $1.92 (!) per test. [2]- Legal Research Bench: 49.04% vs 25.96%, at $0.06 vs $3.22 (!) per test. [3]- HLAB-Vals-1.1: 25% vs 1.67%, at $0.12 vs $1.48 (!) per test. [4]Those are INSANE score differences, at roughly 1/32, 1/54 and 1/12 of Terra's respective benchmark costs!Misleadingly, their overall Vals Index scores are almost identical: 60.48% for Ember and 60.54% for Terra. Terra also scores higher on Vals' Terminal-Bench 4.0 run: 49.49% vs 39.39%. They arrive at similar aggregate scores through quite different strengths. [5][6]For me, this reinforces the case for custom, workload specific evals. Broad benchmarks have been useful for shortlisting models but choosing between them, calls for representative tasks, explicit success criteria and measurements using the tools and reasoning settings you actually intend to deploy.I certainly want to measure cost per successfully completed task, including retries and escalation.Reusing a framework such as Harbor with a custom task suite seems increasingly useful here. [7] The aggregate score here gives me a starting point but the tasks I actually need done then determines my choice.Goign by the generic benchmarks would confuse me to no end and likely burn me out.Great times to be in![1] https://artificialanalysis.ai/leaderboards/models[2] https://www.vals.ai/benchmarks/fabv2-1[3] https://www.vals.ai/benchmarks/legal_research[4] https://www.vals.ai/benchmarks/hlab[5] https://www.vals.ai/models/meta_muse-ember[6] https://www.vals.ai/models/openai_gpt-6.1-terra[7] https://github.com/harbor-framework/harbor
• mgdev
  I can't wait for Muse Smolder. It's gonna somehow run on an ESP32 compatible SOC w/a custom TPU at 100 tok/sec and people are still gonna drag it.
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  gopalv
  Model refusals should be a -ve score, not zero?Treating the refusal as a 0 score erases the difference between lack of capability and a lack of willingness to perform.
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  v9v
  Why no comparisons with DeepSeek?
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  mococa
  Not open weight? Meh... I'll wait for the Chinese releases :)
• dom96
  Super frustrating that I still can’t give Muse Wonder Full a spin.
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  BatchJob
  it is our most modestly priced receptacle...
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  cheesecompiler
  Kind of burnt out on all the model updates at this point.
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  Starlevel004
  Luna 6 is getting a bit long in the teeth now. It's two full months old, but it was only a slight step above Luna 5.6, which is already four months old (Imagine saying this about anything else). So I'm not too surprised that a newer small model is beating it handily. I've been hoping for Luna 6.1/2 as the next model release for a few days now.
• system2
  When a statement starts with "our," I always expect bullshit. Batch pricing is two times cheaper with Haiku. Benchmarks are nearly tied. Nobody uses the dumbest models for coding either.If you want customers to switch over, you better make the price at least half of your competitor's.
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  aaroninsf
  Never Meta.Seriously. Moral harm is real and there is no excuse.
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  caaqil
  I'm always a bit surprised to see Meta talking about models. They have become (almost*) entirely irrelevant to the LLM race, which is weird because their AI lab used to be decent before LLM was cool.What happened to zuck? Was the metaverse this destructive?(*) Feeling charitable today
• johnnyApplePRNG
  Good for summarizing your tweets or what?! Is this a model for ants?!