Union Alpha on OpenRouter: An Honest Scorecard for the Free Stealth Model
stealth/union-alpha landed September 16 — free, anonymous, multimodal, 262k context. I put it through a scripted battery, compared fingerprints against named peers, and kept the transcripts. The engine is usable. The envelope has sharp edges. And we can prove more about what it is than the marketing copy admits.
What the registry actually claims
Pulled live from OpenRouter's model API on 2026-09-17:
| Claim | Value |
|---|
| Model id | stealth/union-alpha |
| Context window | 262,144 |
| Max output | 131,072 |
| Modalities | text+image → text |
| Tool calling / structured output | both listed |
| Reasoning controls | none exposed |
| Pricing | $0 / $0 (preview) |
| Tokenizer field | "Other" |
| Listed | 2026-09-16 |
Two rows matter immediately. Context is a 256k-class window, not a million. And there is no reasoning dial in the public parameter list — if someone tells you to "turn thinking down," that control is not on this route.
How I tested
Ground truths are computed by the script: products, letter counts, vault codes planted in a haystack the script built, shapes and serials rendered into a PNG the script drew. The grader is regex and arithmetic. Structural prompts also ran against named models on my Ollama Cloud shelf and same-gateway OpenRouter peers so exclusions have neighbors, not vibes.
- Objective reasoning — 847×396, chained ops with a decimal midpoint, letter count, changed-numbers bat/ball trap
- Structured output — strict
json_schema extraction, three runs
- Tool calling — single, parallel (Tokyo + Paris + 12*9), forced
tool_choice=calculator on "just say hello"
- Long context — needles at 10%/50%/90% plus a phantom honesty probe, laddered at ~50k / ~180k / ~250k / ~300k
- Vision — ground-truth image with large code, small serial, four shapes
- Latency / streaming — TTFT and completion shape on a ~250-word technical paragraph
- Tokenizer fingerprint — six adversarial strings vs GLM-5.3-Flash, GLM-5.3, Qwen3.5, DeepSeek-V4-Flash, Kimi-K2.7-Code, MiniMax-M3, Gemma4 via Ollama Cloud; same-gateway OpenRouter vs
qwen/qwen3.5-plus; local llama3.1:8b; and raw Meta-Llama-3-8B/tokenizer.json pairwise deltas
What held up
| Test | Result | Detail |
|---|
| Reasoning battery | 4/4 | 335412; 86.95; letter count 12; ball = 10 cents. All clean. |
| Vision | 5/5 | OX-7391, serial KX-4417, red circle / blue rect / yellow triangle / green line. Wall time 1.89s. |
| Single tool call | PASS | get_weather({"city":"Tokyo"}) |
| Parallel tool calls | PASS | Three separate calls: Tokyo, Paris, 12*9. No duplicate-key collapse. |
| Exact format follow | PASS | Five-line LINE1..LINE5 contract honored. |
| Long-context retrieval | clean through ~250k | Needles hit at 54,856 / 179,807 / 250,447 reported prompt tokens. |
| Honesty at depth | perfect | Phantom needle answered NONE at every successful rung. |
Parallel tools matter for agents: three clean function calls with legal JSON, no duplicate-key collapse. Long context matters too: the listing is 262k, and inside that window retrieval holds. At ~250k reported tokens — all three needles, honest NONE on the phantom. That is not "context theater." That is a working 256k-class window.
Where it broke
1. Strict JSON schema hard-fails (0/3)
The registry lists response_format. I sent a strict schema for an order extraction. Three runs. Three provider 400s from Stealth — a hard reject:
{"error":{"message":"Provider returned error","code":400,
"metadata":{"raw":"ERROR","provider_name":"Stealth"}}}
Different failure mode, same practical rule: do not trust structured-output parameters on this route. Ask for JSON in the prompt, parse, validate yourself.
2. Forced tool_choice is ignored
Forced calculator on "Just say hello to me. Do not check anything." Response:
Hello! 👋 Nice to meet you. How can I help you today?
Zero tool calls. Finish reason stop. Correct social manners, wrong contract.
On Ollama Cloud the same forced-choice prompt was also ignored by GLM-5.3-Flash, GLM-5.3, DeepSeek-V4-Flash, Kimi-K2.7-Code, and MiniMax-M3. So on that shelf this failure is not unique to union. Treat forced choice as unverified until your own stack proves it.
3. The "reasoning effort" dial does nothing here
I sent reasoning: {effort: low|high|max} on the same multiplication. Answers were correct all three times. Token shape was not:
| Effort claimed | Completion tokens | Reasoning tokens reported | Wall |
|---|
| low | 49 | 0 | 10.99s |
| high | 52 | 0 | 10.86s |
| max | 52 | 0 | 10.69s |
Union-alpha swallows the parameter. That matches the registry: no reasoning controls exposed. Do not budget as if you can turn thinking down.
4. Past the listed window, it fails honestly
At a ~300k target (past the 262,144 listing), the route returned HTTP 400 after retries — not an empty 200. Honest cliff, still a hard stop. Cap prompts under the listed 262k with margin; I would stay under ~200k for production agent loops until you measure your own workload.
5. Interactive latency is the tax
Streaming a ~250-word technical paragraph:
- Time to first visible token: 44.63s
- Visible text: ~1,746 characters
- Reported completion tokens: 2,669 (most of that is invisible thinking / billing, not the paragraph you read)
- Effective visible throughput on wall clock: roughly ~10 tokens/sec class, not a snappy chat model
Warm literal PONG came back in ~6–8s. Fine for batch and agent steps that can wait. Not what you want for a human sitting on the other side of a blinking cursor.
Head-to-head on the Ollama Cloud shelf
Same structural prompts, named models I already run — including the Qwen shelf, because if the vision tower smells Qwen-family you still have to check whether the whole model is Qwen:
| Model | 847×396 | Parallel tools | Forced tool_choice |
|---|
| stealth/union-alpha (OpenRouter) | ✓ 335412 | ✓ | ✗ ignored |
| glm-5.3-flash:cloud | ✓ (with thinking) | ✓ | ✗ ignored |
| glm-5.3:cloud | ✓ (with thinking) | ✓ | ✗ ignored |
| qwen3.5:cloud | △ thinking-heavy / empty content at 256–512 cap | ✓ | ✗ ignored |
| qwen3.5:397b-cloud | △ same shape as qwen3.5:cloud | ✓ | ✗ ignored |
| deepseek-v4-flash:cloud | ✓ | ✓ | ✗ ignored |
| kimi-k2.7-code:cloud | ✓ (with thinking) | ✓ | ✗ ignored |
| minimax-m3:cloud | ✓ (with thinking) | ✓ | ✗ ignored |
Retired on this host today (HTTP 410): qwen3-vl:235b-cloud, qwen3-vl:235b-instruct-cloud, qwen3-next:80b-cloud, qwen3-coder-next:cloud. So the live Qwen comparison is the 3.5 line, not the old VL SKUs.
Read the table for what it is: union-alpha is not uniquely broken on forced choice in this Ollama sample, and not uniquely strong on parallel tools either — GLM and Qwen3.5 both emit clean Tokyo/Paris/12*9 calls. The structural scorecard above is about union; the shelf just keeps "broken everywhere" vs "broken here" honest.
So what is it? What we can prove vs what we still can't
I wanted a fingerprint with a flat residual — the kind of proof that survives a second look — not a vibes ranking. Here is what actually locked, and what bounced off Stealth's accounting.
Proven in this lab (concrete)
1. Vision tower billing matches the Qwen2-VL family formula — exactly. I sent solid red PNGs at eight sizes and recorded OpenRouter prompt_tokens:
| Image size | prompt_tokens | max(22, ceil(H/28)²+6) | pt − formula |
|---|
| 32 | 20 | 22 | −2 |
| 64 | 20 | 22 | −2 |
| 96 | 20 | 22 | −2 |
| 128 | 29 | 31 | −2 |
| 192 | 53 | 55 | −2 |
| 256 | 104 | 106 | −2 |
| 384 | 200 | 202 | −2 |
| 512 | 365 | 367 | −2 |
Eight sizes. Residual after a constant −2 text overhead: 0.0 mean abs, 0.0 stdev. That is a real fingerprint hit. The vision encoder is Qwen2-VL-family shaped. That does not mean the whole model is stock Qwen — only that the image token accounting matches that tower family (28px patch merge curve).
2. Stealth text prompt_tokens are not a trustworthy tokenizer. Same probe, eight repeats:
- English pangram: mostly 27, once 9
- Digit run: mostly 84, once 199
- Russian sentence: stable 36 in that window — but an earlier run reported 2 for the same string
Two counting modes flipping under identical prompts. That is why a Llama-3 text proof via usage keeps failing: the meter lies often enough to destroy pairwise delta matching. Same-gateway residuals vs meta-llama/llama-3.3-70b-instruct, z-ai/glm-5.3-flash, qwen/qwen3.5-27b, and deepseek/deepseek-v4.1-flash were all non-flat; pairwise hits topped out at 6/36 (Llama) vs 2/36 (GLM) / 1/36 (Qwen). Not a hit.
3. Hard exclusions still hold on the discriminative axes. When union sits on its common digit mode (~84), named peers on the same OpenRouter day land elsewhere: GLM-5.3-Flash 139, Qwen3.5-27b 210. Ollama Cloud digit deltas against union were GLM +52, Qwen3.5 +123/+127. Not GLM-5.3-Flash. Not stock Qwen3.5 text. Not Claude/GPT/Grok.
Not proven (and I will not pretend otherwise)
Text vocab species — Llama-3 vs private/other — is not locked by this battery. The llama3.1:8b 8.8/−10 row is not a fingerprint. Local Meta-Llama-3 tokenizer.json pairwise vs Stealth usage is not a fingerprint while the counter flips 27↔9. Llama-3-class text remains a candidate (and matches what stronger external probes have claimed), not a scorecard win from our usage method.
My call, stated plainly:
- Proven here: vision tower is Qwen2-VL-family (billing curve, 8/8, residual 0).
- Proven here: Stealth text token accounting is bimodal / unreliable (stability flips).
- High confidence exclusions: not GLM-5.3-Flash, not stock Qwen3.5 text, not Claude/GPT/Grok.
- Best synthesis: a cross-lineage multimodal — Qwen2-VL-class vision + a non-Qwen3.5 text backbone (Llama-3-class or private), unnamed post-train, Stealth front. That is an assembly claim, not a public SKU name.
- Exact weights / vendor: still unnamed until a reveal.
If you're running it today
- Use it for agent loops that need clean parallel tool calls and can tolerate multi-second to multi-ten-second waits.
- Do not use
response_format / strict json_schema on this route. Prompt for JSON, validate locally.
- Do not depend on forced
tool_choice. Design the prompt so the tool call is the natural next step.
- Cap context under ~200k even though 250k retrieved in lab. Leave headroom for tools and system prompts.
- Expect the meter. Free stealth previews end. Architect like a price row (or a rename) shows up.
- Validate one call end-to-end before any batch. Free endpoints hide cost; they do not hide broken contracts.
The whole thing in five lines
# the engine is real
reasoning 4/4, vision 5/5, needles clean through ~250k, honest NONE
# the envelope still bites
json_schema -> HTTP 400 forced tool_choice -> ignored
reasoning.effort -> theater interactive TTFT -> ~45s on long answers
# proven positive fingerprint
vision billing = Qwen2-VL formula max(22, ceil(H/28)^2+6) [8/8 sizes, residual 0]
# proven that text usage ID is blocked
Stealth prompt_tokens flip (pangram 27<->9, digits 84<->199)
# exclusions still solid
not GLM, not stock Qwen3.5 text, not Claude/GPT/Grok
# the discipline
don't guess -- inspect
Related reading
Keith is a database consultant and infrastructure engineer with 25+ years of open-source experience. He builds the ApocryiaAI brand and DRAVEN work under 3k1o LLC (apocryia.com). He writes about MySQL, Proxmox, AI memory, multi-agent workflows, and building technology you can actually inspect.
