Muse Spark vs GPT-6.1 Sol: Cost per Task Says the Opposite of Cost per Token

Put M​use Spark and GPT-6.1 Sol on the same line and the per-token pricing makes the decision for you: $1.25 against $2.00 per million input tokens, $4.25 against $10.00 per million output. M​use Spark looks like the cheap one by a wide margin. Then you look at what Artificial Analysis charges to actually complete a single Intelligence Index task with each and the order flips — $0.72 for GPT-6.1 Sol at its max reasoning setting against $1.60 for M​use Spark 1.3 at max. Sol spends less than half as much per finished task while carrying a four-point higher index score, and the only reason is that it is quiet: Sol generated 67M tokens running the index, and M​use Spark 1.3 generated 170M. If you are still costing these two models with a per-token spreadsheet, this matchup is the clearest case in the current field where that spreadsheet is wrong.

What each of these actually is

GPT-6.1 Sol is O​penAI's positioning play in the G​PT-6 line: released September 29, 2026, priced explicitly below the flagship GPT-6 Astra — O​penAI's announcement frames it as matching Astra's intelligence on agentic coding, computer use and professional work at roughly one-fifth of Astra's standard input and output prices. It is generally available, not deprecated, and supported by O​penAI's published model list.

M​use Spark 1.3 is the current checkpoint of M​eta Superintelligence Labs' M​use Spark family, released September 2, 2026 and given the max reasoning setting on September 4. Muse Spark 1.1 and 1.2 precede it. For anyone arriving from the older comparison material, "M​use Spark" without a number now means 1.1, 1.2 and 1.3 collectively, and 1.3 is the one M​eta labels current.

So this is a matchup between two models built around the same pitch — near-flagship capability at a deliberately lower price — which makes the divergence in how they deliver it more interesting than the scores.

The independent numbers, and the one that decides

Both rows below are Artificial Analysis' own hosted measurements at each model's maximum reasoning setting. These are not vendor figures.

• Intelligence Index — GPT-6.1 Sol (Max): 52, rank 12 of 217. M​use Spark 1.3 (Max): 48, rank 24 of 217.

• Cost per index task — GPT-6.1 Sol: $0.72. M​use Spark 1.3: $1.60. Sol is 2.2x cheaper per completed task.

• Output tokens during the index — GPT-6.1 Sol: 67M, described as fairly concise against a median of 87M. M​use Spark 1.3: 170M, described as very verbose.

• Output speed — GPT-6.1 Sol: 53.5 tokens per second, ranked 154 of 186, described as notably slow. M​use Spark 1.3: 121.9 tokens per second, ranked 43 of 186, notably fast. M​use Spark is 2.3x faster out.

• List pricing as measured — Sol at $2.00 in / $10.00 out with cached input at $0.10; M​use Spark 1.3 at $1.25 in / $4.25 out with cached input at $0.15.

There is a genuine trade hiding inside those five bullets, and it is not the one the headline suggests. Sol is the cheaper model to run a complete task with, and it does that by thinking less and talking less — 67M tokens is roughly two-fifths of M​use Spark's output for four more index points. M​use Spark will get an answer to you faster in wall-clock terms once it starts, roughly twice as fast, but it will have generated far more text doing it. Which of those you want depends on whether your constraint is the invoice at the end of the month or the latency in front of a user.

As a sanity check on the direction, LMArena's text leaderboard places a GPT-6.1 Sol configuration at rank 22 with 1484 Elo; M​use Spark 1.3 does not appear on that board. The four-point index gap and the human-preference gap point the same way here, which is not always the case.

Vendor claims on either side

O​penAI published its GPT-6.1 Sol results mostly as deltas rather than absolute scores, which is worth knowing before anyone quotes them as a table. What the announcement asserts, all vendor-reported and unreproduced by us: Sol matches GPT-6 Astra on DeepSWE v1.1 at about a fifth of the cost and beats GPT-6 Sol's best score by 6.4 percentage points at lower reasoning effort; it scores above Claude Opus 5.5 at less than half the cost per task on GDP.pdf; it improves on O​pus 5.5 by 2.2 points on AutomationBench at medium effort; it beats GPT-6 Sol by seven points on OSWorld 2.0's offline set at max effort while coming within 2.1 points of Astra at roughly a seventh of the cost; and at low reasoning effort it cuts the share of responses containing a factual error from 11.4% to 7.7%. The only absolute number in O​penAI's scientific-workflow claim is Astra's own 68.1% on Terminal-Bench Science 0.1, with Sol costing $5.47 per task against $23.21 for Claude Opus 5.5 — a cost comparison, not a score comparison.

M​eta's published figures for M​use Spark 1.3 are DeepSWE v1.1 at 75.4, OSWorld 2.0 at 66.9, Terminal-Bench 2.1 at 76.4 with the Terminus 2 agent, MCP-Atlas at 89.2, GDPval-AA v2 at 1,754, SciCode at 58.5, HLE-Diamond without tools at 26.1 and JobBench at 61.8. Note carefully what cannot be done with these two lists: O​penAI reports changes, M​eta reports levels, and the two vendors did not run the same evaluations on the same harnesses. The one place they nearly meet is DeepSWE v1.1, and even there one number is a delta and the other an absolute.

The specification differences that matter more than the scores

• Input modalities — M​use Spark 1.3 accepts text, image and video. GPT-6.1 Sol accepts text and image only; audio and video are documented as unsupported. For any workflow with video in, this comparison ends here.

• Context window — Sol publishes 1,050,000 tokens with a maximum input of 922,000 and a maximum output of 128,000. M​use Spark 1.3 is a flat 1,048,576 with no published max-output figure we could verify. These look equivalent and behave differently: Sol's window is explicitly carved into input and output budgets, while M​use Spark's is not documented that way.

• Long-context pricing — this is the operational trap on Sol's side. Standard rates apply to prompts up to 272,000 input tokens; above that threshold the request is billed at long-context rates of $4.00 in and $15.00 out, double the input price and 1.5x the output price, for the whole request. A Sol agent that drifts past 272K tokens does not pay a little more, it changes price tier. M​use Spark 1.3 has no comparable tiering in its published rates.

• Service tiers — Sol comes with batch and flex at 50%, fast mode at 2x, and an ultrafast tier at 6x standard pricing, added on October 8, 2026. M​use Spark 1.3 has a standard tier and a Contributor variant that trades a lower price — $0.10 in, $0.20 out — for permission to train on your prompts and completions. That last option has no O​penAI equivalent and no G​oogle equivalent, and for some teams it is disqualifying while for others it is a 10x cost reduction.

• Reasoning controls — Sol's effort parameter runs low, medium, high, xhigh and max, and explicitly does not support "none" or "minimal" — you cannot turn reasoning off. M​use Spark's ladder runs from minimal upward, and M​eta's tooling notes reasoning cannot be disabled either. Both vendors have decided this for you.

• Knowledge cutoff — Sol publishes April 30, 2026. M​eta does not publish a comparable figure for M​use Spark 1.3 that we could verify.

What the cost-per-task number does and does not tell you

The $0.72 against $1.60 figure is the most useful single number in this comparison and it is also the most easily misused. It is the mean cost of running one Intelligence Index task at maximum reasoning effort, which is a specific mixture of reasoning-heavy and long-output evaluations — not your workload. If your application is one short question and one short answer, your tokens-per-request are tiny and M​use Spark 1.3's $1.25 input rate wins outright. If your application is an agent that reasons for a long time and writes a lot of intermediate text, verbosity is the dominant cost driver and Sol's 67M tokens against M​use Spark's 170M is the difference between a sustainable bill and an unpleasant one. The honest way to use that pair of numbers is as a warning that per-token rates rank these two models one way and completed-task economics rank them the other, and only your own token distribution tells you which ranking you live under.

Getting both onto one key

GPT-6.1 Sol is on OrcaRouter, and so are the two Muse Spark checkpoints we serve — Muse Spark 1.2 and Muse Spark 1.1 — at each provider's list price with 0% markup passed through. One API across 200+ models means the verbosity question above is answerable empirically rather than theoretically: log token counts for a few hundred real requests through a single endpoint, then use the routing DSL to send the long-reasoning share of your traffic to whichever side actually wins on your distribution, with automatic failover covering the model that loses. Since Sol's long-context tier flips the price at 272K input tokens, that failover also gives you somewhere to put a request that would otherwise cross the threshold.

The caveat we owe you: M​use Spark 1.3 is not on our catalogue, so the M​use Spark side of that test runs on 1.2, which Artificial Analysis measures one point below M​use Spark Open and which now carries a deprecation notice pointing at 1.3. And every vendor number in this piece — O​penAI's deltas, M​eta's scorecard — is a claim, not a measurement; the only figures here we would defend as independent are the Artificial Analysis rows.

The decision, stated plainly

On the evidence, GPT-6.1 Sol wins this matchup on the metrics that usually decide enterprise adoption: higher independent index score, roughly 2.2x cheaper per completed task, half the output volume, a documented maximum output, and published service tiers you can trade speed against price with. M​use Spark 1.3 wins on the three things a per-task average hides: it accepts video, it is about 2.3x faster once generating, and its pricing does not change tier when a prompt grows. If your workload is video-in or latency-sensitive, the cheaper per-task model is the wrong model. If it is long-horizon reasoning with a lot of written output, the per-token table is lying to you and Sol is the better buy by a factor of two.

Comparisons like this one are also where the failure modes show up: the documented AI incidents and agent failures behind models in this class are collected in the OrcaRouter AI Incident Archive, where each entry carries its own sources, severity, confidence grade and disputed flag.