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  4. Motif 3
MT
Motif Technologies
Released July 14, 2026

Motif 3

Motif 3 by Motif Technologies is a 314B sparse MoE with 13B active, 256K native context, Grouped Differential Latent Attention, and self-speculative decoding.

Announcement
Inputs
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Model Overview

Capabilities, design details, and architectural traits

Motif 3 - Fully In-House Sparse MoE with Novel Attention Architecture

Motif 3 is a large-scale Mixture-of-Experts language model built from the ground up by Motif Technologies, explicitly described as a fully in-house proprietary design rather than a re-parameterization of existing open-source architectures. The model introduces several custom components not found in other major MoE systems.

TraitDetail
Grouped Differential Latent Attention (GDLA)Custom attention mechanism designed in-house for this model, not derived from existing architectures
Grouped PolyNorm activationNovel activation function applied per expert, specific to Motif 3's architecture
Multi-Token Prediction (MTP) headSingle-layer MTP head built into the model, enabling self-speculative decoding during inference
384 routed experts, top-8 plus 1 sharedSparse routing activates 8 of 384 experts per token with an additional shared expert, yielding ~13B active of ~314B total parameters
256K native context262,144-token context window built natively into the architecture
Modified mHCCustom modification of mHC component, part of the in-house design

Self-Speculative Decoding Workflow

The built-in MTP head allows Motif 3 to perform self-speculative decoding without a separate draft model. In vLLM, this is activated through a --speculative-config flag with num_speculative_tokens: 1 identified as optimal for this model. The model also ships with custom reasoning and tool-call parsers (--reasoning-parser motif, --tool-call-parser motif) for structured output.

Benchmark Performance

Independent evaluations · Artificial Analysis

47.4%
Intelligence
63.5%
Coding Index
37.6%
Agentic Index

Accuracy & Capability Details

GPQA - Graduate Science83.4%
Humanity's Last Exam37.0%
SciCode - Scientific Coding40.6%
Long Context Reasoning72.3%
Specs
Context window
262Ktokens

Prices in USD.

Compare Models Side-by-Side

Evaluate specifications, pricing, and independent benchmark indices

Model Details
General Info
ProviderMotif TechnologiesAnthropicAnthropic
Release DateJuly 14, 2026July 24, 2026June 9, 2026
Knowledge Cutoff-May 2026-
Context & Limits
Context Window262K
1M
Best Context Window
1M
Best Context Window
Pricing (per 1M tokens)
Input Pricing
Free
Best Input Pricing
$5$10
Output Pricing
Free
Best Output Pricing
$25$50
Modalities
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Outputs
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Benchmarks (0-100)
Intelligence Index45.3
63.1
Best Intelligence Index
62.1
Coding Index62.0
78.0
Best Coding Index
76.5
Agentic Index34.9
59.2
Best Agentic Index
56.6
Motif 3
Claude Opus 5
Claude Fable 5

GPQA Benchmark

Graduate-level reasoning and expert Q&A evaluation.

87%
Motif 3
GPQA Benchmark
Score: 87%
MT
Motif 3
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.

40%
Motif 3
Humanity's Last Exam
Score: 40%
MT
Motif 3
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.

71%
Motif 3
Long Context Reasoning
Score: 71%
MT
Motif 3
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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