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  4. Inkling
Thinking Machines
Released July 15, 2026

Inkling

Inkling by Thinking Machines: 975B open-weights MoE, 41B active, native text-image-audio input, 1M context, relative attention, and self-fine-tuning on

Visit Thinking MachinesAnnouncement
Inputs
Text
Outputs
Text

Model Overview

Capabilities, design details, and architectural traits

Inkling - Open-weights multimodal MoE with relative attention

Inkling is a Mixture-of-Experts transformer, released under Apache 2.0 by Thinking Machines. It is the first model in a planned family, pretrained on 45 trillion tokens of text, images, audio, and video.

TraitDetail
Relative attentionUses learned relative position encoding instead of RoPE; each attention layer has a fourth projection producing per-token, per-head relative features modified by key-query distance
Hybrid sliding-window attentionLayers alternate in a 5:1 pattern of sliding-window to global attention
Sparse MoE with shared experts256 experts per layer, 6 routed plus 2 shared experts active on every token; 66-layer decoder
Native multimodal encodingImages via hierarchical patch encoder, audio via discrete token encoding, all projected into a shared hidden space
Controllable thinking effortBalances cost with performance through efficient and adjustable reasoning depth
Self-fine-tuning on TinkerDemonstrated writing, running, and evaluating its own fine-tuning job through the Tinker platform
1M token contextSupports up to 1 million tokens across all input modalities

Designed for customization

Inkling is explicitly positioned not as the strongest overall model but as an open-weights base optimized for fine-tuning. It ships with BF16, MXFP8, and NVFP4 checkpoints, the latter including speculative multi-token prediction (MTP) layers for faster inference. The model is accessible through Tinker for fine-tuning and through third-party inference providers.

Benchmark Performance

Independent evaluations · Artificial Analysis

42.3%
Intelligence
52.1%
Coding Index
34.1%
Agentic Index

Accuracy & Capability Details

GPQA - Graduate Science87.2%
Humanity's Last Exam31.9%
SciCode - Scientific Coding46.1%
Long Context Reasoning73.3%
Specs
Context window
1Mtokens
Input pricing
$1per 1M tokens
Output pricing
$4.05per 1M tokens
Cached input
$0.17per 1M tokens

Prices in USD.

Compare Models Side-by-Side

Evaluate specifications, pricing, and independent benchmark indices

Model Details
General Info
ProviderThinking MachinesAnthropicAnthropic
Release DateJuly 15, 2026July 24, 2026June 9, 2026
Knowledge Cutoff-May 2026-
Context & Limits
Context Window1M1M1M
Pricing (per 1M tokens)
Input Pricing
$1
Best Input Pricing
$5$10
Output Pricing
$4.05
Best Output Pricing
$25$50
Modalities
Inputs
text
textimage
textimagefile
Outputs
text
text
text
Benchmarks (0-100)
Intelligence Index42.3
63.1
Best Intelligence Index
62.1
Coding Index52.1
78.0
Best Coding Index
76.5
Agentic Index34.1
59.2
Best Agentic Index
56.6
Inkling
Claude Opus 5
Claude Fable 5

GPQA Benchmark

Graduate-level reasoning and expert Q&A evaluation.

87%
Inkling
GPQA Benchmark
Score: 87%
Inkling
93%
Claude Opus 5
GPQA Benchmark
Score: 93%
Claude Opus 5
93%
Claude Fable 5
GPQA Benchmark
Score: 93%
Claude Fable 5

Humanity's Last Exam

Extremely difficult logical reasoning and knowledge.

32%
Inkling
Humanity's Last Exam
Score: 32%
Inkling
55%
Claude Opus 5
Humanity's Last Exam
Score: 55%
Claude Opus 5
56%
Claude Fable 5
Humanity's Last Exam
Score: 56%
Claude Fable 5

Long Context Reasoning

Logical reasoning over long context windows.

73%
Inkling
Long Context Reasoning
Score: 73%
Inkling
76%
Claude Opus 5
Long Context Reasoning
Score: 76%
Claude Opus 5
77%
Claude Fable 5
Long Context Reasoning
Score: 77%
Claude Fable 5

Independent evaluation data provided by Artificial Analysis. To view the latest benchmarks and full details, visit their official site.

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