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Models › Inkling family

Inkling

Hugging Face ↗ Follow changes

thinkingmachines Apache 2.0 out 3 months ago

952B parameters (6 of 256 experts per token), images and text in, text out, 1M tokens of context. 4 providers serve it from $0.95 per million input tokens.

Verified 2026-10-25 Rank #113 in LLMs · score 97.7 method v1 Sources 4 Downloads 30d 1.6M · #80 of 619 tracked

Can I run it?571GB at INT4Estimateneeds a multi-GPU nodemethodCommunity: Inkling – Open-Weights 975B Parameter LLM →
What does it cost?$0.95/1M incheapest of 2DeepInfra2026-10-25
Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Do people use it?1.6Mdownloads, 30 days#80 of 619 we track · top 13%+9 likes, downloads +18% this week1,826 likesHugging Face2026-10-24

Worth reading

Picked by rule from 10 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 1,227 HN pts

Inkling: Our Open-Weights Model

Thinking Machines releases Inkling, a Mixture-of-Experts model with 975B total parameters and 41B active parameters that supports a 1M token context window.

  • The model was evaluated on Design Arena's Agentic Web Dev leaderboard where blinded human evaluators compare generated web apps head to head.
thinkingmachines.ai 2026-07-15 1,227 HN pts · 291 comments 4.1k words sells inference

10 best of 10 tracked

The 3 picks above, plus these 7. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleInkling – Open-Weights 975B Parameter LLMthinkingmachines.ai · 2026-07-15 · 121 HN pts
  2. ArticleInkling-Smallthinkingmachines.ai · 2026-07-30 · 33 HN pts
  3. ArticleInkling Model Cardthinkingmachines.ai · 2026-07-15 · 7 HN pts
  4. ArticleInkling: A New Open-Weight 975B MoE with a Few Surprisessebastianraschka.com · undated
  5. ArticleInkling’s 975B MoE: The Open-Weight Model That Ditched RoPE and Won’t Stop Thinking - Banandrebanandre.com · 2026-07-20
  6. ArticleInkling review: is Thinking Machines' open model worth it?eesel.ai · undated
  7. ArticleInkling AI Model Review: Benchmarks, Pricing & Full Verdictorcarouter.ai · undated

Where to run it

Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.

2 providers from $0.95

2 providers with a live price, from $0.95 to $1.00 per million input tokens; the median is $0.97. One dot per provider, cheapest in teal. 1 free-tier row not drawn.

$0.010$0.10$1.00$10.00DeepInfra: $0.95 in / $4.05 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/thinkingmachines/Inkling?expand[]=inferenceProviderMappingTogether AI: $1.00 in / $4.05 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/thinkingmachines/Inkling?expand[]=inferenceProviderMappingcheapest $0.95median $0.97
$0.010$0.10$1.00$10.00DeepInfra: $0.95 in / $4.05 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/thinkingmachines/Inkling?expand[]=inferenceProviderMappingTogether AI: $1.00 in / $4.05 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/thinkingmachines/Inkling?expand[]=inferenceProviderMappingcheapest $0.95 · median $0.97 (white tick)
#Cheapest 4In /1MOut /1MContextQuantSeen
1 DeepInfra $0.95 $4.05 512K — Oct 25
2 Together AI $1.00 $4.05 512K unknown Oct 25
3 Thinking Machines · free tier $0 $0 1M nvfp4 Oct 24
4 Fireworks AI — — 1M — Oct 25

All 4 providers →

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

Served by 4 API providers; 48 quantized builds for local use.

  • As a service4DeepInfra, Fireworks AI, Thinking Machines, Together AI
  • On own hardware48quantized builds
  • Built on2715 fine-tunes · 12 adapters · 0 merges
  • In apps37Hugging Face Spaces

Top Spaces running it: ArtShumov/Whyx-PROmpTea ♥ 3 · akhaliq/Inkling ♥ 3 · lyffseba/xai ♥ 1 · Andersong/inkling-demo ♥ 1

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Needs an 8×80 GB node even at INT4

About 571 GB at INT4, 1.14 TB at FP8, 2.29 TB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 571 GB of weights (estimate; KV cache and activations extra)INT4571 GBFP8: about 1.14 TB of weights (estimate; KV cache and activations extra)FP81.14 TBBF16: about 2.29 TB of weights (estimate; KV cache and activations extra)BF162.29 TB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 571 GB of weights (estimate; KV cache and activations extra)INT4571 GBFP8: about 1.14 TB of weights (estimate; KV cache and activations extra)FP81.14 TBBF16: about 2.29 TB of weights (estimate; KV cache and activations extra)BF162.29 TB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

Most coverage came in the first two weeks

Teal is the weights (2026-07-14), amber triangles are 7 dated articles (each links to it), grey ticks are repo commits.

AugSepOct2026-07-23: 1 repo commit2026-07-20: 1 repo commit2026-07-19: 2 repo commits2026-07-16: 2 repo commits2026-07-15: 7 repo commitsInkling: Our Open-Weights Model · 2026-07-15Inkling – Open-Weights 975B Parameter LLM · 2026-07-15Inkling-Small · 2026-07-30Inkling Small is the highest-scoring open-weight model evaluated by ARC Prize · 2026-07-30Inkling Model Card · 2026-07-15Inkling’s 975B MoE: The Open-Weight Model That Ditched RoPE and Won’t Stop Thinking - Banandre · 2026-07-20Inkling | Awesome Agents · 2026-07-15Weights 2026-07-14 · 2026-07-14Weights 2026-07-14now · 4 providers
AugSepOct2026-07-23: 1 repo commit2026-07-20: 1 repo commit2026-07-19: 2 repo commits2026-07-16: 2 repo commits2026-07-15: 7 repo commitsInkling: Our Open-Weights Model · 2026-07-15Inkling – Open-Weights 975B Parameter LLM · 2026-07-15Inkling-Small · 2026-07-30Inkling Small is the highest-scoring open-weight model evaluated by ARC Prize · 2026-07-30Inkling Model Card · 2026-07-15Inkling’s 975B MoE: The Open-Weight Model That Ditched RoPE and Won’t Stop Thinking - Banandre · 2026-07-20Inkling | Awesome Agents · 2026-07-15Weights 2026-07-14 · 2026-07-14Weights 2026-07-14now · 4 providers

What people built on it

75 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

75 derivatives on the Hub, most of them quantized

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

48 quantized (Hugging Face model tree)15 fine-tunes (Hugging Face model tree)12 adapters (Hugging Face model tree)48 quantized15 fine-tunes12 adapters
48 quantized (Hugging Face model tree)15 fine-tunes (Hugging Face model tree)12 adapters (Hugging Face model tree)48 quantized15 fine-tunes12 adapters
GGUF unsloth/inkling-GGUF for llama.cpp, Ollama, LM Studio 1.6M downloads MLX pipenetwork/Inkling-MLX-4bit for Apple silicon 1.3k downloads NVFP4 thinkingmachines/Inkling-NVFP4 for vLLM, SGLang 576.3k downloads

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

30 rows

Benchmarks 30 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (30)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported AA Omniscience1.0% percent—model author2026-10-07
author-reported AIME 2026 · Reason through hard problems97.1% percent—model author2026-10-07
author-reported Audio MC56.6% percent—model author2026-10-07
author-reported BrowseComp (w/ Ctx)77.1% percent—model author2026-10-07
author-reported CharXiv · Extract text from documents78.1% percent—model author2026-10-07
author-reported CharXiv · Extract text from documents82.0% percent—model author2026-10-07
author-reported FORTRESS (Adversarial)78.0% percent—model author2026-10-07
author-reported FORTRESS (Benign)95.9% percent—model author2026-10-07
author-reported GDPVal-AA v21233—model author2026-10-07
author-reported GPQA Diamond · Reason through hard problems87.2% percent—model author2026-10-07
author-reported Global-MMLU · Work across languages88.7% percent—model author2026-10-07
author-reported HLE (with tools)46.0% percent—model author2026-10-07
author-reported Humanity's Last Exam · Reason through hard problems29.7% percent—model author2026-10-07
author-reported IFBench · Chat and follow instructions79.8% percent—model author2026-10-07
author-reported MCP Atlas74.1% percent—model author2026-10-07
author-reported MMAU77.2% percent—model author2026-10-07
author-reported MMMU-Pro · Understand images and video73.5% percent—model author2026-10-07
author-reported SWE-bench Pro · Write code with agents54.3% percent—model author2026-10-07
author-reported SWE-bench Verified · Write code with agents77.6% percent—model author2026-10-07
author-reported SimpleQA · Reason through hard problems43.9% percent—model author2026-10-07
author-reported StrongREJECT98.6% percent—model author2026-10-07
author-reported Tau 3 Banking23.7% percent—model author2026-10-07
author-reported Terminal-Bench · Write code with agents63.8—model author2026-10-07
author-reported VoiceBench91.4% percent—model author2026-10-07
author-reported MathArena/aime_202697.1—Model Card2026-10-24
Show all 30 rows (5 more)
BenchmarkValueSetting / notesReported byDate
author-reported diamond (Idavidrein/gpqa)87.2—Model Card2026-10-24
author-reported hle (cais/hle)46—Model Card2026-10-24
author-reported mmmu_pro_standard_10_options (MMMU/MMMU_Pro)73.5—Model Card2026-10-24
author-reported SWE_Bench_Pro (ScaleAI/SWE-bench_Pro)54.3—Model Card2026-10-24
author-reported swe_bench_%_resolved (SWE-bench/SWE-bench_Verified)77.6—Model Card2026-10-24

What the card says

1 cited sentence

Summary generated, cited

Inkling is a 952B parameter multimodal autoregressive transformer designed for text, image, and audio inputs.[1][2]

  1. parameters human
  2. “Multimodal autoregressive transformer”

Generated 2026-10-07 15:01 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

7 use cases

What it is good at 7 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

21 fields

Listed in LLMs · MoE

Facts

Pipeline tagimage-text-to-text
Librarytransformers
ArchitecturesInklingForConditionalGeneration
Model typeinkling_mm_model
Dtypebfloat16
Params by dtypeBF16: 952,377,607,178 · F32: 16,448
Size bandXXL
MoEyes
Experts256
Experts per token6
Context length1,048,576 (provider listing)
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · InklingForConditionalGeneration · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · InklingForConditionalGeneration · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for Inkling: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)2.29 TB · Cluster (≥640 GB)
FP8 (1 byte/param)1.14 TB · Cluster (≥640 GB)
INT4 (0.5 bytes/param)571 GB · Multi-GPU node (<640 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

All variants

40 variants

Lineage

Original model Family: thinkingmachines/Inkling 75 derivatives on the Hub

quantized: 48finetune: 15adapter: 12

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
unsloth/inkling-GGUF quantized GGUF — — apache-2.0 unsloth 137 1.6M
thinkingmachines/Inkling-NVFP4 quantized NVFP4 553B XXL apache-2.0 thinkingmachines 98 576.3k
lightseekorg/Inkling-MXFP4 quantized MXFP4 527B XXL apache-2.0 lightseekorg 0 26.8k
RedHatAI/Inkling-FP8-dynamic quantized -FP8 953B XXL apache-2.0 RedHatAI 1 5k
mlx-community/Inkling-mlx-4bit finetune mlx 947B XXL apache-2.0 mlx-community 7 4k
mlx-community/Inkling-mlx-2bit finetune mlx 947B XXL apache-2.0 mlx-community 6 3.8k
inference-optimization/Inkling-0.6B-A0.6B finetune — 644M S mit inference-optimization 1 2.9k
AtomicChat/Inkling-GGUF quantized GGUF — — apache-2.0 AtomicChat 4 2.6k
pipenetwork/Inkling-MLX-4bit quantized MLX 947B XXL apache-2.0 pipenetwork 1 1.3k
unsloth/inkling-NVFP4 finetune NVFP4 553B XXL apache-2.0 unsloth 11 739
avlp12/Inkling-975B-Alis-MLX-Dynamic-3.7bpw quantized MLX 947B XXL apache-2.0 avlp12 2 930
RedHatAI/Inkling-NVFP4-FP8-BLOCK quantized NVFP4 952B XXL mit RedHatAI 1 884
meshllm/Inkling-MTP-BF16-GGUF-beta quantized -BF16 — — apache-2.0 meshllm 4 625
DevQuasar/thinkingmachines.Inkling-GGUF quantized GGUF — — — DevQuasar 0 732
sabrewing-engine/Inkling-colibri-int4 quantized int4 — — apache-2.0 sabrewing-engine 3 486
pipenetwork/Inkling-MLX-REAP50-4bit quantized MLX 483B XXL apache-2.0 pipenetwork 0 633
pipenetwork/Inkling-MLX-REAP25-4bit quantized MLX 715B XXL apache-2.0 pipenetwork 0 582
pipenetwork/Inkling-MLX-REAP12-4bit quantized MLX 835B XXL apache-2.0 pipenetwork 0 561
thunquant/Inkling-NVFP4 quantized NVFP4 553B XXL apache-2.0 thunquant 0 556
DragonBophades/Inkling-Gutenberg-DPO finetune — 952B XXL apache-2.0 DragonBophades 1 475
biosai-hf/Inkling-MXFP4 quantized MXFP4 527B XXL apache-2.0 biosai-hf 0 473
RedHatAI/Inkling-FP8-block quantized -FP8 952B XXL apache-2.0 RedHatAI 1 394
RedHatAI/Inkling-NVFP4 finetune NVFP4 553B XXL apache-2.0 RedHatAI 1 380
avlp12/Inkling-975B-Alis-MLX-Dynamic-2.7bpw quantized MLX 947B XXL apache-2.0 avlp12 0 384
trinityomni/Inkling-NVFP4 quantized NVFP4 553B XXL apache-2.0 trinityomni 0 375
biennequants/Inkling-NVFP4 quantized NVFP4 553B XXL apache-2.0 biennequants 0 374
tbmod/Inkling-mini-bf16 finetune -bf16 16.5B L apache-2.0 tbmod 0 356
yujiepan/inkling-tiny-random finetune — 3.66M S — yujiepan 0 345
schwyzquants/Inkling-NVFP4 quantized NVFP4 553B XXL apache-2.0 schwyzquants 0 329
Pq234/inkling-GGUF quantized GGUF — — apache-2.0 Pq234 0 294
bielquants/Inkling-NVFP4 quantized NVFP4 553B XXL apache-2.0 bielquants 0 266
avlp12/Inkling-975B-Alis-MLX-Dynamic-6.6bpw quantized MLX — — apache-2.0 avlp12 0 255
baerquants/Inkling-NVFP4 quantized NVFP4 553B XXL apache-2.0 baerquants 0 250
konizquants/Inkling-NVFP4 quantized NVFP4 553B XXL apache-2.0 konizquants 0 233
winterthurquants/Inkling-NVFP4 quantized NVFP4 553B XXL apache-2.0 winterthurquants 0 199
RobinArena/TradeFinder-1 adapter — — — apache-2.0 RobinArena 2 60
RampPublic/portal-inkling adapter — 41.6M S apache-2.0 RampPublic 1 93
baselquant/Inkling-MTP-BF16-GGUF-beta quantized -BF16 — — apache-2.0 baselquant 0 131
fribourgquant/Inkling-NVFP4 quantized NVFP4 553B XXL apache-2.0 fribourgquant 0 125
shaffhausenquant/Inkling-NVFP4 quantized NVFP4 553B XXL apache-2.0 shaffhausenquant 0 120

Repository changelog

13 commits

Repository changelog 13 commits

DateCommitSHA
2026-07-23 Update README.md 828496e
2026-07-20 Update README.md 32b58a6
2026-07-19 Add community evaluation results for AIME_2026, GPQA, HLE, MMMU_PRO, SWE-BENCH_PRO, SWE-BENCH_VERIFIED (#12) e9ad070
2026-07-19 Update README.md 4d72dbf
2026-07-16 Add transformers tag for stats (#5) 86b4d43
2026-07-16 Update README.md 81ef5d2
2026-07-15 Update README.md e4aa5ee
2026-07-15 Update README.md 9b18fa9
2026-07-15 Update README.md 89b7d5e
2026-07-15 Transformers processor and chat template support (#2) 0992a1e
2026-07-15 Update README.md d058aec
2026-07-15 Update README.md 499a0d8
2026-07-15 Model release 91b051f

All providers

4 providers · 4 endpoints

Where to run 4 providers · 4 endpoints

Hosted endpoints observed for Inkling, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting. Cheapest observed input price: $0.00/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
Thinking Machines
openrouter
 thinkingmachines/inkling 1M nvfp4 $0.00 $0.00 — — yes live 2026-10-24
DeepInfra
hf
 thinkingmachines/Inkling 512K — $0.95 $4.05 142.2 695 ms yes live 2026-10-25
Together AI
hf, openrouter
 thinkingmachines/Inkling 512K unknown $1.00 $4.05 86.9 208 ms yes live 2026-10-25
Fireworks AI
hf
 accounts/fireworks/models/inkling 1M — — — 92.7 1391 ms yes live 2026-10-25

Articles and coverage

10 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Analysis9

Inkling: Our Open-Weights Model
thinkingmachines.ai2026-07-15HN front pageHN 1,227 pts · 291 comments4,123 words
Thinking Machines releases Inkling, a Mixture-of-Experts model with 975B total parameters and 41B active parameters that supports a 1M token context window. The model was pretrained on 45 trillion tokens and is available for fine-tuning on Tinker alongside a preview of Inkling-Small with 12B active parameters. Inkling features an encoder-free architecture for audio and vision inputs and supports controllable thinking effort, spending one third as many tokens as Nemotron 3 Ultra to achieve the same performance on Terminal Bench.written, reviewed, cited
  • The model was evaluated on Design Arena's Agentic Web Dev leaderboard where blinded human evaluators compare generated web apps head to head.
Inkling – Open-Weights 975B Parameter LLM
thinkingmachines.ai2026-07-15HN front pageHN 121 pts · 4 comments216 words
Inkling provides efficient open models for text, images, and audio available to fine-tune on Hugging Face or via the Tinker training platform. The most capable variant contains 975B total parameters with 41B active, while the smaller size has 276B total and 12B active parameters. Both models support a context of 1M tokens and match larger siblings on benchmarks at a quarter of the size.written, reviewed, cited
Inkling-Small
thinkingmachines.ai2026-07-30HN 33 pts · 2 comments2,416 words
Thinking Machines releases Inkling-Small, an efficient open-weights Mixture-of-Experts model with 276B total parameters and 12B active trained on NVIDIA GB300 NVL72 systems. The model features a context window of up to 1M tokens and achieves comparable performance to Inkling at a quarter of its size across agentic tool use (Terminal-Bench 2.1), reasoning (HLE text-only), and instruction following (IFBench) benchmarks. It is available for fine-tuning on Tinker and for text, image, and audio chat on Tinker Playground, with variable thinking effort to balance cost and performance.written, reviewed, cited
  • Inkling-Small surpasses Inkling on reasoning and agentic coding benchmarks despite Inkling maintaining an advantage on knowledge coverage and factuality.
  • The model uses an encoder-free architecture where images are divided into 40×40-pixel patches and transformed using a four-layer hMLP.
  • ForecastBench and Prophet Arena results were obtained during testing between July 19 and July 28, 2026.
Inkling Model Card
thinkingmachines.ai2026-07-15HN 7 pts · 0 comments1,832 words
Thinking Machines Lab released Inkling, a 66-layer decoder-only transformer where each token is routed to 6 of 256 experts, on July 15, 2026. The model features 975B total parameters with 41B active and supports BF16, MXFP8, and NVFP4 numerics. Running the BF16 checkpoint requires a GPU cluster with at least 2 TB of aggregated VRAM, while the NVFP4 quantized alternative reduces this requirement to at least 600 GB. It accepts text, image, and audio inputs and supports a context window of up to 1M tokens under an Apache 2.0 license.written, reviewed, cited
  • Running W4A4 mode on NVIDIA B300 GPUs additionally requires SM100+ architecture.
  • The model is designed to be used by developers building agentic and tool-use systems.
Inkling: A New Open-Weight 975B MoE with a Few Surprises
sebastianraschka.comauthority854 words
Inkling’s 975B MoE: The Open-Weight Model That Ditched RoPE and Won’t Stop Thinking - Banandre
banandre.com2026-07-201,792 words
Inkling | Awesome Agents
awesomeagents.ai2026-07-151,801 words
Inkling review: is Thinking Machines' open model worth it?
eesel.ai2,001 words
Inkling AI Model Review: Benchmarks, Pricing & Full Verdict
orcarouter.ai3,878 words

Community1

Inkling Small is the highest-scoring open-weight model evaluated by ARC Prize
twitter.com2026-07-30HN 6 pts · 0 comments

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Sources

4 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:34 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:01 UTC · Last verified 2026-10-25

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