Ox Alpha
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Now in stealth · free to try
Meet Ox Alpha.
The reasoning model that appeared out of nowhere. Built for code, long-horizon agents, and a million tokens of context. Nobody knows who made it — everyone wants to try it.
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1M<br>tokens of context
131K<br>max output tokens
input modalities<br>text · image · video
$0<br>while it's in stealth
Reasoning
Thinks before<br>it speaks.
Ox Alpha is a reasoning-first model. It plans, checks itself, and only then answers — so hard problems get worked through instead of guessed at.
Multi-step logic Math, systems design, proofs, tricky edge cases — worked through, not pattern-matched.
Visible thinking Watch the reasoning stream in before the final answer lands.
Production-grade output Tuned for real workloads, not party tricks.
1,048,576
A million tokens of memory.
Paste an entire codebase, a 500-page spec, or a week of transcripts. Ox Alpha keeps all of it in mind while it works — no chunking, no retrieval tricks, no losing the thread.
Capabilities
Built for real work.
Everything a frontier model should do — and a few things most of them can't.
Long-horizon coding
Designed for sustained software engineering: debugging, refactoring, and reasoning across large codebases without losing context.
Sees what you see<br>Text, images, and video in. Screenshots, diagrams, and UI — reasoned about, not just described.
Tool calling
Native tools and tool_choice support for agents that actually do things.
Structured output
Ask for JSON and get JSON — schema-shaped responses via response_format.
Sustained agentic work
Keeps its footing across long, multi-step tasks instead of drifting off halfway.
In action
Watch it reason.
A reasoning model shows its work. Here's what a typical exchange looks like.
oxalpha.com/chat
Why does this Python function sometimes return stale data?<br>cache = {}; def get(k): return cache.get(k) or fetch(k)
Ox Alpha
Thinking Two issues: or treats falsy cached values (0, "", []) as misses and re-fetches; and nothing ever writes to cache, so the "stale" data must come from fetch itself — likely a module-level connection reused across requests. Check both.
The bug is in the or : any falsy cached value is treated as a miss. Also, cache is never populated. Use an explicit membership check and store results:<br>def get(k):<br>if k not in cache: # explicit miss check<br>cache[k] = fetch(k) # actually populate<br>return cache[k]<br>If results still look stale, the culprit is fetch reusing a connection with read-consistency lag — want me to look at it?
Benchmarks
Stealth model. Top of the table.
On an independent run of 10 real-world coding tasks, Ox Alpha solved 8 — ahead of every frontier model it was lined up against.
80%<br>Ox Alpha<br>8 of 10 tasks solved
65%<br>Fable 5<br>fable-5 [max]
62%<br>GLM-5<br>glm-5.3 [max]
52%<br>GPT-5<br>gpt-5.6-sol [max]
62%<br>Grok 4<br>grok-4.6 [xhigh]
Task<br>fable-5 [max]glm-5.3 [max]gpt-5.6-sol [max]grok-4.6 [xhigh] ox-alpha
anko-typed-variable-bindings<br>4/44/42/41/4 ✓
arktype-json-schema-refs<br>2/41/43/41/4 ✓
fastapi-deprecation-headers<br>4/43/43/44/4 ✓
helm-unified-manifest-stream<br>4/44/44/44/4 ✓
igel-persist-feature-schema<br>3/43/40/44/4 ✓
katex-multicolumn-array-spans<br>2/44/43/44/4 ✓
meriyah-explicit-resource-decl<br>1/40/40/40/4 ✓
query-persist-restored-state<br>2/43/41/42/4 ✓
scc-bounded-memory-spilling<br>4/43/44/44/4 ✕
vulture-persistent-analysis-cache<br>0/40/41/41/4 ✕
Mean on these 10<br>65%62%52%62% 80%
Independent community benchmark — 10 real-world coding tasks. Reference models: passes out of 4 attempts per task. Ox Alpha: pass/fail. Highlighted row: the task every reference model scored 1/4 or worse on — Ox Alpha solved it. Third-party data, small sample — directional, not definitive.
Full breakdown & methodology
Ox Alpha vs Fable 5
Ox Alpha vs GLM-5
Ox Alpha vs GPT-5
Ox Alpha vs Grok 4
Try it on
Pick a task. Or bring your own.
Coding
Debug a gnarly production traceback
Root-cause analysis from a model built for long-horizon software engineering.
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Reasoning
Work through a hard math or logic problem
It plans before it answers — watch it think in steps, not guesses.
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Big context
Analyze an entire codebase in one prompt
A 1M-token window means the whole repo fits. No chunking, no RAG.
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Vision
Explain a screenshot, chart, or diagram
Combine text with visual context and let it reason about what it sees.
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Agentic
Design a multi-step agent workflow
Tool calling, structured output, and the stamina for long tasks.
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Coding
Review a pull request like a senior engineer
Correctness, edge cases, simplifications — with the reasoning behind them.
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Writing
Turn rough notes into a production-ready doc
Structured, precise writing tuned for production workloads.
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Big context
Digest a 500-page PDF into key takeaways
Executive summary, quotes, and...