Muse Code vs Qoder Cantus: The Agent With a Price List vs the Model With a Multiplier

Six days apart, two vendors made opposite bets on how you should pay for an autonomous coding agent. On August 1, 2026, the half-price promotion on Qoder Cantus — Alibaba's top-tier coding model, reachable only from inside the Qoder platform — quietly lapsed, and its billing coefficient snapped back from 1.6x to 3.2x. On August 5, Meta shipped Muse Code, a terminal coding agent running on Muse Spark 1.2, with published per-token rates and a contributor tier priced at roughly a hundredth of what a Cantus turn now costs. One of these two products tells you exactly what a token costs. The other tells you a multiplier and asks you to trust the meter.

That is the actual comparison, and it is not the one the category names suggest. Everything below that carries a benchmark number comes from Meta's own launch charts, published without methodology, except where an independent measurement is named. For Cantus there is nothing to label, because Alibaba has published no benchmark, no context window, no parameter count and no technical report — a fact that turns out to be structural rather than an oversight.

You are not comparing two of the same thing

Muse Code is an agent you install and a model you rent. One command puts the binary on your machine — curl -fsSL https://dev.meta.ai/install.sh | bash — and from there it bills against the Meta Model API by the token. Muse Spark 1.2 is also sold separately through that API, so the model and the harness are two purchases you can make independently.

Qoder Cantus is not a product you can buy. It is an entry in a dropdown. Qoder describes it as its "built-in top-tier intelligent model, excelling at extended autonomous task execution," and that one sentence is the entire official description — there was no launch post, only a pricing promotion on July 19. To use Cantus you subscribe to Qoder, open Qoder, and pick Cantus from the model tier selector. There is no API endpoint, no downloadable weights, and no way to point an existing tool at it.

The consequences of that asymmetry run through every section that follows, so it is worth setting the two side by side plainly:

• What you install — Muse Code: a terminal binary, macOS and Linux only, public beta. Qoder Cantus: nothing; it is a setting inside the Qoder desktop IDE, JetBrains plugin, CLI, cloud agents or mobile/web client.

• Operating systems — Muse Code: macOS and Linux, no native Windows build. Qoder: Windows, macOS and Linux, since the 1.0 release on May 15, 2026.

• Billing unit — Muse Code: input, cached-input and output tokens, no subscription, no cap. Qoder Cantus: credits from a monthly plan, multiplied by a per-model coefficient, capped at whatever the plan includes.

• Model outside the tool — Muse Spark 1.2: yes, through the Meta Model API in public preview. Cantus: no, by design.

• Published context window — Muse Spark 1.2: 1,048,576 tokens. Cantus: undisclosed. Qoder's docs list 200K, 400K and 1M options depending on which model you select, but attach no figure to Cantus.

• Published benchmarks — Muse Spark 1.2: four boards, all Meta-run. Cantus: none, from anyone.

Price, converted into the same currency

Meta's rates are arithmetic. The standard tier is $1.25 per million input tokens, $0.15 per million cached input and $4.25 per million output; the contributor tier, where you let Meta use the session data to improve its models, is $0.10, $0.002 and $0.20. Take a realistic agent step — 60,000 tokens of repository context in, 3,000 tokens of plan-and-patch out — and the standard tier costs 8.8 cents cold, 2.2 cents when the repo context hits the cache, and the contributor tier costs 0.66 cents cold or 0.072 cents warm.

Qoder's rates take a conversion. A credit is worth one cent on the plans that matter: Pro is $20 for 2,000 credits, Ultra is $200 for 20,000, both exactly $0.01. Qoder publishes credit estimates per task — roughly 12 credits for an Editor agent-mode turn at 200K of context, about 50 for a Quest agent-mode task, about 75 for Quest in Experts mode — and separately publishes the coefficient that multiplies them. Cantus sits at 3.2x. Reading those two published numbers together, which Qoder never does in a worked example, an Editor agent turn on Cantus is about 38 credits, or 38 cents. A Quest task is about 160 credits, $1.60. Quest in Experts mode is about $2.40.

So one Cantus agent turn costs roughly four times a cold Muse Code step, about eighteen times a cached one, and around fifty-eight times a contributor-tier step. The context sizes are not identical — Qoder's estimate is quoted at 200K against Meta's 60K in our step — and Qoder is explicit that its table is statistical estimation rather than a quoted price. Even granting both caveats generously, the gap does not close to anything like parity.

Two details make the Cantus side worse than the headline. The first is that the discount is gone. Cantus launched "beyond Ultimate," which is to say at double the 1.6x coefficient of Qoder's previous top tier, and the July 19–31 promotion sold it at exactly the Ultimate rate. That window closed at 23:59 on July 31. Anyone who evaluated Cantus in late July benchmarked a price that no longer exists. The second is overflow: run out of plan credits and top-ups cost $20 for 1,500 credits, which is $0.0133 a credit rather than $0.01, pushing a Cantus Editor turn past 51 cents once you exceed your allowance.

What Qoder buys you in exchange is a ceiling. $20 a month is $20 a month; when the credits are gone the platform drops you to basic models and your bill stops. Muse Code has no ceiling at all — Meta's own 24-hour, 1,000-tool-call demonstration run works out to roughly $88 of standard-tier tokens for a single job, and nothing in the product stops the second one. Predictable spend versus cheap spend is a genuine trade, not a rhetorical one, and small teams have picked the capped side before for good reasons.

This is also the section where our own bias should be stated rather than hidden: OrcaRouter bills at 0% markup, passing provider list price through unchanged, which is a pricing philosophy much closer to Meta's published rates than to a credit coefficient. It is also why we cannot quote you a Cantus price — there is no list price to pass through, only a multiplier applied to a private token count.

One vendor published the losses. The other published nothing — and can't.

Meta's launch deck is unusually self-incriminating. On Terminal-Bench 2.1 it reports Claude Opus 5 in Claude Code at 86.7% and Muse Spark 1.2 in Muse Code at 82.9%, with GPT-5.6 Terra in Codex at 81.8% and Grok 4.5 at 81.6%. On DeepSWE 1.1 it reports 59.3% for Muse Spark 1.2 against 65.0% for Claude Opus 5 and 64.8% for GPT-5.6 Terra. On Meta's own internal coding benchmark, the fixture Meta built and controls, it reports 70.6% against Claude Opus 5's 79.4%. Second or third on every board Meta chose to show, including its home one. Independently, Artificial Analysis measured Muse Spark 1.2 at 54 on its Intelligence Index against 51 for Muse Spark 1.1, bought at a time-to-first-token of 26.12 seconds versus 2.90 — a real, externally-verified number and an unflattering one.

Against that, Cantus offers a single adjective: "top-tier." Not one benchmark, not one independent evaluation, no Artificial Analysis entry, no LMArena placement, no leaderboard row anywhere.

The important part is that this is not a documentation backlog Qoder will clear next quarter. Independent evaluation requires programmatic access, and there is no Cantus API. A third party who wanted to score it would have to drive a desktop IDE by hand or automate its UI, against a model whose identity, version and routing behaviour can change under them silently between sessions. Cantus is, as shipped, unbenchmarkable by anyone outside Alibaba — and that is a permanent property of the distribution choice, not a temporary state.

Which cuts both ways, and the honest version of this comparison has to say so. A benchmark deck you lose is still more information than no deck at all, but 82.9% on Terminal-Bench also does not tell you whether Muse Code will handle your monorepo, and plenty of teams have found that the model with the worse leaderboard row is the one that finishes their tickets. The difference is that with Muse Code you can find that out cheaply and reversibly, and with Cantus the only evaluation instrument available is a $20 subscription and your own eyes.

Both stake their pitch on surviving the long job

Strip away price and scores and the two products are chasing the same workload with strikingly similar machinery, which is the most interesting convergence in this matchup.

Muse Code's answer is a local event log. Every model call, tool run, approval and edit is appended to disk, which Meta describes as making a session replay-exact and restart-safe: crash the process or lose the machine, and the agent resumes where it stopped rather than reconstructing its context. On top of that sit what Meta calls persistent async background agents — subagents that stay alive across the session and fan out into isolated git worktrees rather than being spawned and torn down per subtask, which is how Meta says it built six game features in parallel without collisions. The supporting evidence is a single case study: a 24-hour run of more than 1,000 tool calls autonomously optimizing GPU kernels on NVIDIA Hopper hardware, with no published speedup figure. The duration is the claim, not the result.

Qoder's answer is Quest Mode, and the vocabulary rhymes. Quest is a dedicated window for handing off long-running multi-step work, with task tracking, artifact review, checkpoints and local worktree execution, and it is exactly the mode Cantus is sold for — "extended autonomous task execution." Qoder also brings something Meta has no equivalent for: a Knowledge Engine that builds a Repo Wiki and Knowledge Cards from your codebase, so the agent starts a long task with accumulated repository context rather than rediscovering it. That is a real architectural difference in the same problem space, and Qoder has been shipping it since 1.0 in May.

Neither claim is independently verified. Meta's event log has not been stress-tested by anyone outside Meta, and Qoder's Quest results have not been measured by anyone at all. If long-horizon reliability is your deciding factor, both vendors are currently asking for the same thing: trust.

The switching cost is where they truly diverge

Every model in Qoder's selector except one can be walked away from. Qwen3.7-Max, DeepSeek V4 Pro, GLM-5.2, Kimi-K2.7-Code and MiniMax-M3 all sit in that dropdown at their own coefficients, and all of them exist outside Qoder — you can call them from your own code, price them against each other, and take your prompts with you. Cantus is the only entry on that list that is a one-way door. Build a team workflow around it and the exit cost is not a config change; it is the discovery that no other product can run the model you standardised on.

Muse Code sits in an awkward middle. The model is portable — Muse Spark 1.2 is on the Meta Model API and anything accepting a custom endpoint can call it — but it has exactly one provider, which makes it a single point of failure by construction. And the 82.9% belongs to the model inside Muse Code. Meta's headline claim is that the two were co-trained for "better tool use, fewer retries, and higher-quality output than a generic wrapper around an outside model." If that is true, calling Muse Spark 1.2 from your own harness gets you the model half and none of the tuned half. Nobody outside Meta has tested it in a rival harness to find out how much of the score was the agent.

Being precise about what we can offer here: Muse Spark 1.2 is served by Meta directly and is not in the OrcaRouter catalog, and Cantus cannot be in anyone's catalog. Muse Spark 1.1 is with us, at $1.25 and $4.25 — Meta's list price exactly, because we add 0% markup and pass provider pricing straight through, which is also why a vendor price change appears on our side the day it happens. Our production telemetry for 1.1 shows p50 time-to-first-token at 1.84 seconds and p95 at 6.00 seconds, which is a more useful latency expectation for a chat-shaped workload than any benchmark harness will give you.

The broader point is about where you put the abstraction. If your agent harness talks to one endpoint that fronts many models, with automatic failover across providers, then a coding-agent launch is a string change and a vendor outage is somebody else's problem. If your harness is a vendor's IDE, every one of those decisions belongs to the vendor. Qoder's own Auto tier does model routing internally, at about 1.0x credits, picking the cheapest acceptable model per task — the same idea we sell as infrastructure, with the crucial difference of who holds the steering wheel.

Where your code goes

Meta made this explicit, and it deserves credit for that even though the offer is aggressive. The contributor tier is 12.5x cheaper on input, 21x cheaper on output and 75x cheaper on cached input in exchange for Meta being permitted to use the data to improve its models. A terminal coding agent's prompts are your source, your internal APIs, your comments explaining why the workaround exists and whatever your test fixtures contain. Meta states that standard-tier prompts are not used to improve its products; that is the tier for anything proprietary. Treat the choice as a data-handling decision with a discount attached rather than a billing preference, and note that the incentive to quietly downgrade grows exactly as your token spend does.

Qoder attaches no equivalent condition to the Cantus rate in its public documentation — the discount was time-boxed, not data-boxed, and there is no cheaper tier that trades on training rights. What you are accepting instead is that a closed model of unknown provenance, hosted by Alibaba Cloud, processes your repository, and that the security posture is described in Qoder's own documentation rather than in a benchmark anyone can check. For teams with data-residency requirements that is a procurement question before it is an engineering one, and it points in the opposite direction from Meta's: Meta will tell you exactly what it takes and charge you less for it; Qoder takes no explicit position and offers no discount either way.

Questions this comparison actually raises

Which is cheaper for one developer at realistic volume?

It depends entirely on whether you hit a plan ceiling. Qoder Pro is $20 for 2,000 credits, which at the 3.2x Cantus coefficient is roughly 52 Editor agent turns at 200K of context, or about 12 Quest tasks — and that $20 also buys the IDE, repository indexing, Repo Wiki and every other model in the selector. The same $20 of Muse Code standard-tier tokens buys around 228 cold steps or 900 cached ones, and around 3,000 steps on the contributor tier, but buys no tooling whatsoever. Light users who value the IDE come out ahead on Qoder; anyone running an agent hard enough to blow through 2,000 credits is buying $20-for-1,500-credit top-ups at a 33% worse rate while Meta's marginal token price stays flat.

Can I evaluate them against each other properly before committing?

Not symmetrically, and this is the practical trap. Muse Code can be tested for a few dollars on a scratch clone, and Muse Spark 1.2 can be swapped into an existing harness to isolate the model from the agent. Cantus can only be tested by subscribing to Qoder and working inside it, which means you are evaluating the IDE, the context engine, Quest Mode and the model as one bundle with no way to attribute the result. If Cantus performs well you will not know which part earned it, and if it performs badly you will not know which part to blame.

Is either safe to point at a proprietary repository today?

Muse Code on the standard tier, yes on Meta's stated terms — with the caveat that it is a one-day-old public beta whose crash-safety guarantees nobody outside Meta has verified. Muse Code on the contributor tier, not without whoever signs off on data handling seeing the terms first. Qoder Cantus is a normal commercial IDE decision rather than a novel one, but it is a closed model with no published provenance running on Alibaba Cloud, so it belongs in the same conversation about where source code is permitted to go.

Who should pick which

Pick Muse Code if your work is long-horizon and headless — migrations, dependency upgrades across a monorepo, kernel or build-system work you would otherwise babysit — and if you live in a terminal on macOS or Linux. It is priced like infrastructure, the model is portable, the failure modes are visible in an event log you own, and at contributor-tier rates the cost of finding out is close to zero. Accept that it is beta, that it lost every board its own vendor picked, and that Windows is not supported.

Pick Qoder Cantus if the IDE is the product you actually want. A repository knowledge engine, Quest Mode, JetBrains integration, cloud agents and a Windows build are real advantages that Muse Code does not answer at any price, and a capped monthly bill is worth something to a small team that has been surprised by a metered one. Go in knowing three things: the model is unbenchmarked and unbenchmarkable from outside, the 50% launch discount expired on July 31 so it now costs double what early evaluators paid, and there is no exit path if it becomes central to how your team works.

What would change this analysis is narrow and specific. If Qoder publishes a Cantus API, or any third party manages to put a real number on it, this stops being a comparison between a measured product and an adjective. Until then the strongest argument for Muse Code is not its benchmark scores — it loses those — but that it has scores at all, a public price list, and a door out.