Capital & Compute

Union Alpha Is Pareto 26.9: Price, Benchmarks, Cost per Task

Union Alpha is Unbiased Pareto 26.9, a blended coding model at $2.50/$7.50 per Mtok that ties Astra on DeepSWE. Specs, benchmarks, cost.

· ai· pricing· coding· By Capital & Compute

Union Alpha is Pareto 26.9, a blended AI model from Unbiased (the AI platform of Circuit and Chisel) that runs several frontier and open models on each request and keeps the best answer. It appeared anonymously on September 16, 2026 as stealth/union-alpha, went paid after about 33 hours, and now lists at $2.50 per million input tokens and $7.50 per million output tokens with a 262,144-token context window.

$2.50
Input per Mtok
$7.50 output, $0.25 cached input
262K
Context window
262,144 tokens, 131K max output
74
DeepSWE (vendor run)
Tie with Astra and DeepSeek 4.1 Flash
$2.03
Modeled agentic task
About a fifth of Astra on same workload

What Union Alpha is now

Union Alpha was a stealth listing, and Pareto 26.9 is the product behind it. Unbiased describes Pareto on its homepage as a blended model available through the API, stating that under the hood it runs several models on each request and keeps the best answer. The model card uses the shorter label “blended AI model.” One model string, one bill, several engines per request.

That design answers the identity question in a way the usual guessing game did not anticipate. Community forensics spent the preview hunting for a single lab (GLM, Kimi, and MiniMax theories all circulated), but there was no single set of weights to find. The closest guess, aired on r/opencode during the preview, was that Union Alpha behaved like a parallel mixture or gateway rather than one model. The reveal confirmed the shape of that guess while replacing its mechanism: not a cheap relay over other APIs, but Unbiased’s own selection layer that scores candidate answers and returns the winner.

Unbiased also draws a line between Pareto and a router. A router, in the company’s definition, picks one model per request by guessing at difficulty and breaks prompt cache whenever it switches models mid-conversation. Pareto, the company states, never switches models mid-conversation, so the prompt cache and its savings stay intact. Whether that distinction holds under independent testing is unproven, but it is the load-bearing product claim: the value proposition is continuous model selection without the cache invalidation a naive router would cause.

The company behind it is Circuit and Chisel, a remote-first team across the US and Canada, per the Unbiased homepage. The stealth launch was deliberate: the project has said it released anonymously to observe real user reactions and learn what broke at scale before the planned public launch of version 26.10 next month.

  1. February 2026

    Pony Alpha turns out to be GLM-5

    An anonymous listing claimed within days by Zhipu AI (now Z.ai), establishing the stealth-release playbook this wave follows.

  2. August 20 to 26, 2026

    Ox Alpha becomes GLM-5.3-Flash

    A free anonymous preview on OpenRouter and OpenCode, identified by tokenizer forensics and confirmed by Z.ai with MIT-licensed weights. The full story is in our Ox Alpha explainer and the GLM-5.3-Flash pricing post.

  3. September 16, 2026

    Union Alpha appears, unclaimed

    Free, 262,144-token context, image input, tool calling, provider listed only as Stealth. OpenCode advertises roughly a week of free access.

  4. September 17, 2026

    Free window closes; Pareto 26.9 named

    About 33 hours after listing, OpenRouter names Unbiased Pareto as the model behind Union Alpha and switches it to paid access at $2.50 input, $0.25 cached, $7.50 output per million tokens.

Confirmed specs, and what stays undisclosed

Spec Value
Model ID unbiased/pareto (OpenRouter), pareto (Unbiased API)
Stealth ID (retired) stealth/union-alpha, no endpoints since September 17
Context window 262,144 tokens (256K)
Maximum output 131,072 tokens
Input / output Text and images in; text out
Tool calling / structured output Yes; tools, tool_choice, response_format, no schema enforcement
Reasoning control None exposed
Price $2.50 input, $0.25 cached input, $7.50 output per Mtok

These figures come from the OpenRouter catalog API response for unbiased/pareto, cross-checked against the Unbiased model card and pricing page on September 18, 2026. Just as important is what no one has published: parameter counts (expected, since several models participate per request), the identities of the blended models, training details, or an independent benchmark run. The knowledge cutoff field in the catalog response is null.

Two infrastructure notes matter for anyone wiring this up. First, the stealth ID is dead on OpenRouter: the old stealth/union-alpha page returns a 404 as of September 18 and the catalog carries only unbiased/pareto, so any integration built during the free window needs repointing, not just repricing. Second, Cloudflare still documents the stealth/union-alpha ID, a leftover of the launch-day distribution that has not caught up with the rename.

Benchmarks: a vendor slate, read with the vendor caveat

Unbiased publishes five scores for Pareto 26.9 against Claude Fable 5.1, GPT-6 Astra, and DeepSeek 4.1 Flash. The table below reproduces the model card exactly, wins and losses alike:

Benchmark Pareto 26.9 Fable 5.1 GPT-6 Astra DeepSeek 4.1 Flash
DeepSWE 74 67 74 74
Terminal-Bench 4.0 51 56 58 31
MMMU-Pro 78 81 87 77
HLE (no tools) 49 55 54 39
ArXivMath 88 72 91 28

The honest reading: Pareto ties for the lead on one of five tests (DeepSWE, the agentic coding benchmark most relevant to its stated purpose) and trails on the other four. Its best comparative story is against Fable 5.1 on the two math-flavored sets, ArXivMath (88 against 72) and the coding benchmark that matters for agent work. Against Astra it is level on DeepSWE and behind everywhere else, by 7 points on Terminal-Bench 4.0 and 9 on MMMU-Pro. Against DeepSeek 4.1 Flash it trades wins: level on DeepSWE, far ahead on Terminal-Bench 4.0 and ArXivMath, essentially tied on vision and factuality sets.

All of this is vendor-reported, and Unbiased deserves credit for printing the losses alongside the tie. The model card states plainly that measured task costs and a composite score have not been published for this release, and that scores do not establish cost per completed task. No Artificial Analysis index, no independent leaderboard entry, and no third-party reproduction exists yet. Our DeepSWE versus FrontierCode coverage explains why harness, sampling, and run count matter as much as the headline percentage on agentic sets; until someone reruns the slate, treat 74 as a maker-set ceiling rather than a field result.

The one community data point, a launch-day chart placing Union Alpha at about 73% on DeepSWE at roughly $0.65 per task against about $6.50 for Astra and $11.80 for an Opus-class model, is consistent with the vendor’s 74 but is a chart read reported by the agent platforms MindStudio and Siora, not a neutral measurement. It is a signal, not a verdict.

What it costs per task, modeled

The rate card is where Pareto’s case gets concrete. Against the modeled multi-step agentic workload this site uses across its tracker (3,000,000 input tokens at 90% cache share, 80,000 output tokens, one pass), the arithmetic from live list prices is:

Modeled cost per multi-step agentic task, US dollarsPareto 26.9 costs 2.03 dollars per modeled multi-step agentic task. Opus 5 costs 4.85 dollars. Fable 5.1 costs 7.68 dollars. Astra costs 9.70 dollars.$0$2$4$6$8$10Pareto 26.9$2.03Opus 5$4.85Fable 5.1$7.68Astra$9.7
Modeled cost per multi-step agentic task, US dollars
ItemValue
Pareto 26.9$2.03
Opus 5$4.85
Fable 5.1$7.68
Astra$9.7
Modeled cost of one multi-step agentic task (3M input tokens at 90% cache share, 80K output) at live list prices: Pareto 26.9 at $2.03, Opus 5 at $4.85, Fable 5.1 at $7.68, Astra at $9.70.Source: Unbiased pricing page and model card; registry rows for Astra, Fable 5.1, and Opus 5; computeCost agentic preset

In ratios, Pareto runs about 21% of Astra, 26% of Fable 5.1, and 42% of Opus 5 on this workload shape. That lands inside Unbiased’s own framing on its pricing page: input at 25% of Fable 5’s rate, output at 15%, cached input at 25%, with competitor prices dated September 17, 2026 and published machine-readable in the company’s prices file. On the smaller one-file edit preset (150,000 input tokens at 80% cache, 8,000 output), Pareto costs about $0.17 against about $0.82 for Astra.

The caveat that applies to every modeled figure on this site applies here: token mix is destiny. A workload that misses cache invalidates the cheapest line on the bill, and Pareto’s cache story (never switching mid-conversation) is vendor-asserted, not measured. The AI coding cost calculator prices Pareto against the rest of the tracked field on custom workload shapes, and the AI model tracker keeps its rate card current. For context on how Astra’s higher sticker can still produce a lower bill through token efficiency, see the Astra launch analysis.

How to access it

On OpenRouter, call unbiased/pareto through the standard OpenAI-compatible endpoint with the usual tools, tool_choice, temperature, top_p, and max_tokens parameters. There is a single provider behind the ID, so there are no routing decisions and no failover to configure. On Unbiased’s own platform the model identifier is pareto, sold as pay-as-you-go prepaid credits with new accounts reviewed by hand before API access turns on. Cloudflare documents the old stealth ID; confirm with Cloudflare before pointing production traffic at it, since OpenRouter has already retired that identifier.

Anyone who built on the free preview should assume two breaking changes at once: the endpoint moved and the meter started. The free window ran about 33 hours against the week OpenCode announced, and post-preview pricing was undisclosed until the reveal, which is the standard stealth-preview hazard documented in the Ox Alpha explainer.

The data-terms split to read before sending code

Free compute came with the same privacy split the Ox Alpha preview had. OpenCode’s Zen documentation describes a zero-retention policy with no training use, while the OpenRouter listing for the same model stated that prompts and completions may be retained by the provider but are not used for training, as documented by Progressive Robot and LLM Rumors during the preview. Both statements can be true at once because they describe different doors into the same system, but they are not the same policy.

What it is not

It is not a single lab’s flagship weights release, so fingerprinting-style attribution (tokenizer offsets, error-string formats, refusal patterns) was never going to resolve it the way it resolved Ox Alpha to GLM-5.3-Flash. It is not an open-weights model either: no repository, license, parameter count, or architecture note has been published, and none is promised. And it is not, on current evidence, the cheapest way to finish every task. DeepSeek 4.1 Flash undercuts it substantially per token at off-peak rates, and its vendor-reported Terminal-Bench 2.1 score of 90.6 against Pareto’s Terminal-Bench 4.0 score of 51 (different suite versions, not directly comparable) is a reminder that headline benchmarks cluster around whichever suite flatters the model. The September roundup keeps both rate cards side by side as the month develops.

Bottom line

Union Alpha delivered the standard stealth-preview bargain: a real endpoint, a real free window, and a real mystery, all resolved within 33 hours into a priced product with a vendor scorecard. Pareto 26.9 is worth benchmarking for coding-agent workloads where DeepSWE-like performance at roughly a quarter of Fable 5 input pricing would move the bill, and where a blended selection layer without mid-conversation switching proves out on cache-heavy sessions. Until an independent run reproduces the 74, judge it by completed work on a trial workload, keep it behind a switch rather than hardwired as the only model, and budget against the $2.50, $0.25, and $7.50 rate card rather than the $0 preview that introduced it.

Frequently asked questions

What is Union Alpha?
Union Alpha was the stealth listing name for Pareto 26.9, a blended AI model from Unbiased that runs several frontier and open models per request and keeps the best answer. It appeared anonymously on September 16, 2026 and was named on September 17.
Is Union Alpha still free?
No. The free preview lasted about 33 hours, against the week originally announced. Paid access lists at $2.50 per million input tokens, $0.25 cached input, and $7.50 output.
Who built Union Alpha?
Unbiased, the AI platform built by Circuit and Chisel. The company says it launched anonymously to observe real user reactions and scaling behavior before the planned public launch of version 26.10.
What are the specs?
A 262,144-token context window with up to 131,072 output tokens, text and image input with text output, tool calling, and no exposed reasoning control. Tokenizer is listed as Other and the knowledge cutoff is undisclosed.
How good are the benchmarks?
Vendor-reported only: DeepSWE 74 (tied with Astra and DeepSeek 4.1 Flash), Terminal-Bench 4.0 51, MMMU-Pro 78, HLE without tools 49, ArXivMath 88. No independent reproduction exists, and the model card states task costs and a composite score are unpublished.
How do I call it now?
Use unbiased/pareto on OpenRouter or pareto on the Unbiased platform. The old stealth/union-alpha identifier has no endpoints on OpenRouter; repoint any preview integration and confirm the Cloudflare route before using it.

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