Foresight
Tests how early a model sees threats coming and plans around them.
Identity
Who made Iceman?
OpenCode shows the lab as Unknown, on purpose. The strongest lead, from @aitrackerbot on X, points to Microsoft's MAI. Nobody has confirmed it.
The account reports that Iceman introduces itself as “MAI, created and trained by Microsoft AI”, and that its prompt token counts match MAI Thinking 1 on 20 of 20 tool schemas and 8 of 8 text tests. The same method earlier tied the stealth model Glyph Cluster to Microsoft's MAI.
Our own tokenizer test finds the same exact match with MAI Thinking 1, and an equally exact one with OpenAI's GPT-6 models.
Our research · 25 October 2026
Seven models count tokens exactly like Iceman.
23 / 23
One tokenizer family
On every measured phrase, Iceman's input token count matches MAI Thinking 1, the stealth model Glyph Cluster, OpenAI's GPT-6 models and gpt-oss-120b to the token.
Each model gets the same short phrases in several languages and scripts, plus code, maths, numbers, emoji and whitespace. Models that split text the same way report the same counts.
OpenAI's GPT-6 models count the same way, so this test can't say which lab built Iceman.
| Model | Matches |
|---|---|
| Same tokenizer | |
| MAI Thinking 1 | |
| Glyph Cluster | |
| GPT-6 Astra | |
| GPT-6 Luna | |
| GPT-6 Sol | |
| GPT-6.1 Sol | |
| gpt-oss-120b | |
| Closest of the rest | |
| GLM 5.3 | |
| Kimi K3 | |
| DeepSeek V4 | |
| Grok 4.7 | |
23 of 24 phrases measured.
Benchmarks
Below most models on every test.
Each task asks for a scene written as SVG code. A high score means the model met every requirement, from reflections in a mirrored room to a Mona Lisa drawn from memory.
StealthMark benchmarks, 25 October 2026
Iceman's scores are on the StealthMark benchmark page.
Games
Can Iceman plan ahead?
A high score in these games means the model plans many turns ahead and learns from the levels it has already played.
Latch
Tests planning with a partner and learning from each level.
Access
Free tokens, paid in prompts.
Iceman runs only on OpenCode Go. For a limited time its tokens are free, with no usage cap, but you need a Go subscription, from $10 a month.
You also pay in data. OpenCode offers Iceman “in exchange for permission to use your prompts and completions to improve the model” and keeps your requests. On OpenCode, that trade is what “Contributor” means.
How to get it
- Subscribe to OpenCode Go in the OpenCode Console and copy your API key. OpenCode Go docs ↗
- Under Settings, Privacy, turn on “Allow paid endpoints that train on request data”. Go counts Iceman as a paid endpoint and refuses it until you do.
- In OpenCode, run
/connect, choose OpenCode Go, paste your key and pickopencode-go/.iceman-contributor-free - For direct API calls, use
https://opencode.ai/inference/go/. Send your client's own User-Agent and anopenai/v1/chat/completions x-opencode-sessionheader with one stable ID per conversation, such asses_….
Send it nothing you wouldn't want in a training set.
Model ID: iceman-contributor-free
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Timeline
How Iceman showed up.
A commit adds Iceman Contributor to the models.dev catalog under OpenCode Go. The entry lists a 262,144-token context window and closed weights.
models.dev commit ↗OpenCode merges a docs change that adds Iceman to its Go plan. It is free for a limited time “in exchange for permission to use your prompts and completions to improve the model.”
OpenCode pull request 58792 ↗The same change puts Iceman on OpenCode's stealth list, which hides its lab on the data page.
OpenCode stats code ↗A thread on r/opencode is the first public sighting: Iceman is on the Go page but can't be selected yet.
r/opencode thread ↗@FOMAnews posts the model name and ID on X, the earliest social post we found.
@FOMAnews on X ↗@aitrackerbot reports that Iceman's prompt token counts match Microsoft's MAI Thinking 1 on 20 of 20 tool schemas and 8 of 8 text tests.
@aitrackerbot on X ↗OpenCode's data page counts 10,856 users and about 417 billion tokens for Iceman on 24 October, with the lab shown as Unknown. Traffic had already started on 23 October, before any public listing.
OpenCode data page ↗OpenCode Go accepts four reasoning levels for Iceman: low, medium, high and xhigh. It rejects max.
Our researchIn three runs at low effort on OpenCode Go, Iceman streamed 18 to 62 tokens per second, about 46 on average, and started answering in about 0.9 seconds.
Our researchIceman counts tokens exactly like MAI Thinking 1, the stealth model Glyph Cluster and OpenAI's GPT-6 models.
Our tokenizer test ↑
Latest updates
First benchmark and game results. Scores ↑
Added our tokenizer test, speed runs and reasoning levels. Tokenizer test ↑
@aitrackerbot posts a token-count test. The post ↗
First public sighting, on r/opencode. The thread ↗
OpenCode adds Iceman to the Go docs. The change ↗
Common questions
Is Iceman Contributor actually free?
The tokens are, for a limited time and with no usage cap. You still need an OpenCode Go subscription, from $10 a month, and OpenCode may use your prompts and completions to improve the model. How to get it
Who made Iceman?
No lab has claimed it, and OpenCode lists the lab as Unknown. Two token-count tests, one outside and one ours, give a lead. Read the leads
How good is Iceman?
It scores below most models on all seven of our picture tasks and both planning games. See the scores
What does “Contributor” mean?
On OpenCode it marks a data deal: free or cheaper access in exchange for permission to train on your prompts and completions. Google's Gemini 4 Argon Contributor and Meta's Muse Spark Contributor carried the same label, so it doesn't name a lab.
What are Iceman's specs?
It takes text and returns text. It supports tool calls and four reasoning levels (low, medium, high and xhigh), with a 262,144-token context window and up to 131,072 output tokens. Its weights are closed, and it runs on OpenCode Go only.
Sources
- OpenCode pull request 58792 ↗ Date, Go only, model ID, free and unlimited, the training quote and retention.
- models.dev commit ↗ Specs and closed weights.
- OpenCode data page ↗ 10,856 users, 417 billion tokens, lab Unknown.
- OpenCode stats code ↗ The stealth model list.
- OpenCode Go docs ↗ Go plans from $10 a month, your key, /connect, the session header and earlier Contributor models.
- r/opencode thread ↗ First sighting.
- @FOMAnews on X ↗ Earliest social post.
- @aitrackerbot on X ↗ The token-count test and the model's self-introduction.
- Our research: reasoning levels, speed, the privacy setting and the tokenizer test. Glyph Cluster has its own guide.