What is SubQ and how does it compare to Gemini and Magic LTM for long context?

SubQ, launched in May 2026 by Subquadratic, is the first commercial sub-quadratic LLM with a native 12 million-token context window, targeting 100M by Q4 2026. It uses Subquadratic Selective Attention (SSA) and claims ~1000x compute reduction at 12M tokens vs frontier attention. Gemini 3.1 Pro offers a proven 1-2M token transformer-based context with strong recall and multimodal support. Magic.dev's LTM-2-Mini holds the 100M-token record (since 2024) but is software-development-only and not widely benchmarked in production.

Which one is best for actual long-context coding work in May 2026?

Gemini 3.1 Pro is the safest production choice today — proven recall (~99% on long-context retrieval), 2M token context, real production usage, and the broadest ecosystem. SubQ is the most exciting if it holds up under independent testing — 97% on RULER 128K and 83 on MRCR v2 puts it well ahead of Gemini and GPT-5.4 on long-context retrieval. Magic LTM-2-Mini's 100M context is unmatched on paper but Magic's production deployment outside their own coding agent is still thin.

How does cost compare?

Gemini 3.1 Pro is the cost-leader of the frontier transformer models at $2/M input and $12/M output tokens, with context caching that further reduces long-context economics. SubQ claims roughly 1/5 the cost of leading LLMs thanks to its sub-quadratic architecture — if true at scale, this could reshape the long-context market. Magic.dev's LTM pricing isn't broadly published since it's mostly used inside their own coding tool.

Should I use these instead of RAG?

Long context complements RAG, it doesn't replace it. For codebases up to ~2M tokens you can plausibly skip RAG with Gemini 3.1 Pro and feed the whole codebase. With SubQ's 12M-token window you can fit very large monorepos. But RAG still wins on cost, freshness, source attribution, and indexable knowledge updates. The 2026 pattern is hybrid: large native context for the immediate codebase + RAG for everything else.

Quick Answer

SubQ 12M vs Gemini 3.1 Pro vs Magic LTM Long Context (2026)

Published: May 17, 2026

SubQ 12M vs Gemini 3.1 Pro vs Magic LTM (May 2026)

Long-context LLMs took a major leap in May 2026 when Subquadratic released SubQ, the first commercial sub-quadratic LLM with a 12M-token native window. Here’s how it stacks up against Google’s Gemini 3.1 Pro and Magic.dev’s record-holding LTM-2-Mini.

Last verified: May 17, 2026

TL;DR

	SubQ (1M-Preview / 12M)	Gemini 3.1 Pro	Magic.dev LTM-2-Mini
Native context	12M tokens (100M target Q4 2026)	1M (Pro), 2M (extended)	100M tokens
Architecture	Sub-quadratic (Subquadratic Selective Attention)	Transformer + optimizations	Long-Term Memory mechanism
Launched	May 5, 2026 (early access)	February 2026	August 2024
Multimodal	Text + code	Text + image + audio + video	Text + code (software-focused)
Pricing (per M tokens)	~1/5 frontier cost (early-access)	$2 input / $12 output	Not broadly published
MRCR v2	83	23	n/a
RULER 128K	97%	strong (~94%+)	n/a
SWE-Bench Verified	81.8% / 82.4%	80.6%	n/a (no general benchmark)
Production maturity	Early access	GA, broad ecosystem	Internal Magic coding agent + selected partners

SubQ — the architectural disruptor

SubQ 1M-Preview is Subquadratic’s flagship, launched May 5, 2026 as the first commercial LLM built on a sub-quadratic architecture (Subquadratic Selective Attention, SSA) rather than the O(n²) attention used by transformers.

Key claims:

Native 12M-token context (targeting 100M by Q4 2026).
~52x faster than FlashAttention at 1M tokens.
~1000x less attention compute at 12M tokens vs frontier transformers.
Roughly 1/5 the cost of leading LLMs.
97% on RULER 128K (vs Claude Opus 4.6 at 94%).
83 on MRCR v2 (vs Opus 78, GPT-5.4 39, Gemini 3.1 Pro 23).
92% recall at full 12M-token context.
SWE-Bench: 81.8-82.4% — competitive with Claude Opus 4.7.

What it ships with:

12M-token API in early access.
SubQ Code CLI agent for coding workflows.

Caveats:

Researchers (e.g. via VentureBeat) have demanded independent proof of the 1000x efficiency claims.
Most public benchmarks are at 1M tokens, not the full 12M.
It’s brand new — limited third-party deployment data as of May 17.

Gemini 3.1 Pro — the proven default

Released February 2026, Gemini 3.1 Pro is Google’s flagship long-context production model.

Key facts:

1M-token context as the default, 2M in extended-context mode.
Multimodal (text, image, audio, video) — the only one of the three with full multimodal long context.
MRCR v2: 23. SWE-Bench Verified: 80.6%.
Strong long-context retrieval — ~99% recall on text retrieval at 1M-token range.
$2/M input, $12/M output — the cost-leader among closed frontier models.
Context caching reduces effective cost on repeated long-context queries by ~75%.
Wide ecosystem — Vertex AI, AI Studio, Firebase Studio, on-device Gemini Nano.

Strengths:

Proven in production with millions of developers.
Multimodal is unique at this scale.
Cost-effective, especially with caching.

Weaknesses:

MRCR v2 score (23) lags SubQ’s 83 dramatically — long-context multi-hop reasoning is a weakness vs SubQ.
2M token ceiling until Gemini 3 Pro launches with the rumored 10M context.

Magic.dev LTM-2-Mini — the long-game underdog

LTM-2-Mini was introduced in August 2024 with a 100M-token context window — still the largest publicly-known native context as of May 2026.

Key facts:

100M-token context — ~10M lines of code or ~750 novels.
Long-Term Memory (LTM) mechanism, ~1000x more efficient than vanilla attention at that scale (Magic’s claim).
HashHop benchmark — Magic’s own multi-hop reasoning benchmark for long context.
Focus: software development — used inside Magic.dev’s coding agent.
Limited general-purpose benchmark data.

Strengths:

The largest native context publicly known.
Domain-specialized for code — purpose-built rather than general-purpose.

Weaknesses:

Narrow deployment — mostly visible inside Magic’s own coding product.
Limited public benchmarks beyond HashHop.
No multimodal.
Hasn’t been refreshed at the rate of Gemini or SubQ — LTM-2-Mini is now 21 months old.

Head-to-head

Long-context recall (MRCR v2 / RULER)

SubQ wins — by a wide margin on MRCR v2.
Gemini 3.1 Pro — strong on RULER, weaker on MRCR v2.
Magic LTM-2-Mini — limited public scores; strong on HashHop.

Production readiness

Gemini 3.1 Pro — clear winner. GA, ecosystem, SLAs.
Magic LTM — production-ready inside Magic; limited public availability.
SubQ — early access only.

Cost economics

SubQ — cheapest if claims hold (~1/5 frontier cost).
Gemini 3.1 Pro — cheapest among proven frontier models.
Magic LTM — not broadly priced.

Multimodality

Gemini 3.1 Pro — only true multimodal long-context option.
SubQ — text + code only.
Magic LTM — code-focused.

Effective max context (with caveats)

Magic LTM-2-Mini: 100M tokens.
SubQ: 12M tokens (100M target Q4 2026).
Gemini 3.1 Pro: 2M tokens (Gemini 3 Pro rumored at 10M).

When to use which

Use Gemini 3.1 Pro for:

Production workloads today.
Multimodal long-context (video transcripts, codebases + design assets).
Cost-conscious long-context workflows with caching.

Try SubQ for:

Multi-hop reasoning over very long codebases or documents.
Research and benchmarks where attention compute is your bottleneck.
Early-adopter advantage if the architecture proves out.

Consider Magic LTM for:

Coding tasks where you genuinely need a 100M-token context (giant monorepos).
Magic.dev’s own agent product, where LTM is the underlying engine.

Long context vs RAG in May 2026

The “long context kills RAG” thesis didn’t pan out. In production teams use both:

Native long context for the immediate working set (current PR, current monorepo, the document you’re editing).
RAG for everything else — knowledge bases, fresh documentation, sources you need to attribute, content that changes.

Long context is a complement, not a replacement.

Strengths and weaknesses summary

	Strengths	Weaknesses
SubQ	Best MRCR v2, sub-quadratic = cheap at scale, 12M native	Early access only, claims need independent validation, text-only
Gemini 3.1 Pro	Proven, multimodal, cheap, broad ecosystem	MRCR v2 lags, 2M context ceiling
Magic LTM-2-Mini	100M context, purpose-built for code	Limited public availability, no multimodal, aging

What’s next

SubQ — independent benchmarks at full 12M context; 100M target by Q4 2026.
Gemini 3 Pro — rumored 10M-token context window at Google I/O (May 19-20, 2026).
Magic.dev — LTM-3 has been hinted; details TBD.
GPT-5.5 — expected to expand from current 400K toward 1M+ in H2 2026.
Claude Opus 4.7 / Mythos — Anthropic’s long-context strategy is more conservative; expect 1M-2M, not 12M.

TL;DR

If you need long context today, use Gemini 3.1 Pro. If you’re an early adopter chasing the next architecture, try SubQ. If you live inside a 100M-token monorepo and have Magic.dev access, use LTM-2-Mini. The architectural shift to sub-quadratic attention is the most interesting story in long-context AI in May 2026 — but it still has to prove itself outside the marketing deck.

Sources: eWeek, VentureBeat, DataCamp, Magic.dev blog, Google AI Studio docs, llm-stats.com — May 2026.

SubQ 12M vs Gemini 3.1 Pro vs Magic LTM (May 2026)

TL;DR

SubQ — the architectural disruptor

Gemini 3.1 Pro — the proven default

Magic.dev LTM-2-Mini — the long-game underdog

Head-to-head

Long-context recall (MRCR v2 / RULER)

Production readiness

Cost economics

Multimodality

Effective max context (with caveats)

When to use which

Long context vs RAG in May 2026

Strengths and weaknesses summary

What’s next

TL;DR

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