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Meta’s own launch chart puts Claude Opus 5 ahead of its model — 86.7% against 82.9% on Terminal-Bench 2.1, on Meta’s slide, in Meta’s harness. That settles the capability question before we start and leaves the interesting one: Opus 5 costs four times as much on input and nearly six times as much on output, so is it worth it? Below we work through that with the hosted Muse Spark 1.2 endpoint as the challenger; for the same argument against Anthropic’s other flagship, see Muse Spark 1.2 vs Claude Fable 5.

Vendors do not usually publish the chart where they lose, and that Meta did is worth crediting — it also means the honest comparison starts from a four-point deficit rather than a marketing claim.

The numbers, side by side

• Intelligence Index — Claude Opus 5 (max) 63 vs Muse Spark 1.2 (xhigh) 57, per Artificial Analysis’ live board. Opus 5 currently sits at the top of that board; Muse Spark 1.2 is #12 of 185.

• Price — Opus 5 $5.00 / $25.00 per million tokens vs Muse Spark 1.2 $1.25 / $4.25. That’s 4× on input and 5.9× on output.

• Cached input — Muse Spark 1.2 at $0.15 per million, an 88% reduction, which matters a great deal for repository-context workloads.

• Terminal-Bench 2.1 — 86.7% vs 82.9% on Meta’s own vendor-run chart. Neither model has a verified entry on the official Terminal-Bench leaderboard; the top row there is Claude Code paired with Claude Fable 5 at 83.8%.

• Context window — effectively a tie: 1,048,576 tokens for Muse Spark 1.2, 1,000,000 for Claude Opus 5. Neither model can use “we hold more of your repository” as an argument against the other.

• First-token latency — OrcaRouter’s seven-day production telemetry shows Opus 5 at p50 4.26 s and Muse Spark 1.2 at p50 7.73 s. The more expensive model is also the faster one.

That last line deserves a second look, because it inverts the usual pattern. Normally you trade latency for cost. Here the cheaper model is both weaker on the index *and* slower to first token — it earns its price advantage purely on the rate card.

Where the six points actually show up

An index score is an average, and averages hide the shape of a model. Two independent sources give us that shape.

Vals AI, which runs everything through one common harness, places Muse Spark 1.2 5th of 45 on its Vals Index at 71.88%, at $0.69 per test — the cheapest of anything in its top five. Its per-domain ranks are more revealing still: #1 of 44 on Finance Agent (v2), #1 of 136 on TaxEval v2, #1 of 31 on Harvey’s Legal Agent Benchmark, #9 of 79 on SWE-bench — and #14 of 50 on Terminal-Bench 2.1.

Artificial Analysis shows a similar pattern generationally: the jump from 1.1 to 1.2 was concentrated in agentic evaluations, with GDPval-AA v2 rising 260 Elo points to 1631 (#5).

So the honest summary is not “Opus 5 is better at everything by six points.” It’s closer to: Opus 5 leads on hard, open-ended reasoning and on the terminal-coding work Meta chose to market against, while Muse Spark 1.2 is remarkably strong at structured, document-heavy, long-horizon professional tasks — and cheap at them.

The cost arithmetic, made concrete

Take a workload of 50 million input tokens and 10 million output tokens a month — a realistic figure for a team running an automated code-review or document-analysis pipeline.

• Claude Opus 5 — $250 input + $250 output = $500

• Muse Spark 1.2, standard tier — $62.50 input + $42.50 output = $105

• Muse Spark 1.2, with cached input — around $7.50 + $42.50 = $50, if the bulk of your input is a stable, repeated context

That’s a 5× to 10× spread. At that ratio, the question stops being “which model is better” and becomes “is the six-point gap worth $400 a month on this specific pipeline” — a question only your own evaluation can answer.

Two corrections to that arithmetic, both of which favour Opus 5 more than the rate card suggests. First, Muse Spark 1.2 is verbose: Artificial Analysis measured it consuming 95 million output tokens to complete the Intelligence Index against a ~70 million tier median, and its cost per index task is $0.40. Token-count assumptions carried over from a less deliberative model will under-forecast. Second, retries cost money. A model that gets a hard task right the first time can be cheaper in practice than one that needs three attempts, however favourable its per-token price.

Choosing between them

Pick Claude Opus 5 when the task is genuinely hard and getting it wrong is expensive — architecture decisions, security-sensitive code, subtle debugging, anything where a human will spend an hour verifying the output. Also pick it when latency matters, which is the counter-intuitive part: it answers faster.

Pick Muse Spark 1.2 when volume is the constraint, or when the work is the kind of structured professional analysis where its independent domain results are strongest. Batch pipelines, overnight agent runs and document-heavy workflows are its natural home — and since both models hold roughly a million tokens, the cheaper one lets you actually afford to fill that window.

Use both for anything serious. They are not substitutes and the price gap makes tiering obviously correct: cheap model first, expensive model for the cases the cheap one flags or fails. Behind a single OpenAI-compatible endpoint that carries both at 0% markup, that routing is configuration rather than architecture — and it means you can run a real A/B on your own tasks this week instead of arguing about a benchmark chart.

The takeaway

Meta’s own chart says Claude Opus 5 is the better coding model, and the independent index agrees by six points. What Meta’s chart doesn’t say is that its model costs a fifth as much, holds the same million tokens of context, and — on the only neutral multi-domain harness available — ranks first in finance, tax and legal agent work while placing fourteenth at coding. Opus 5 is the model to reach for when being right matters more than the bill. Muse Spark 1.2 is the one to reach for when you’re doing a lot of structured work at scale and can wait a few extra seconds for each answer. Most teams running both will end up routing between them rather than choosing.

Sourcing note: the 86.7% and 82.9% Terminal-Bench figures are Meta’s own vendor-run results with each model paired to its own agent product, and have not been independently reproduced. Index scores are from Artificial Analysis’ live board; domain ranks and per-test cost from Vals AI; the 83.8% row from the official Terminal-Bench 2.1 leaderboard; pricing and p50 first-token latency from OrcaRouter, whose rates are the providers’ list prices passed through at 0% markup. All checked August 7, 2026.

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