Meta dropped Muse Spark 1.1 recently. Curiously, the timing tells you everything about where this market is right now. It’s not launching into a quiet field — it’s launching directly into the middle of a fight Anthropic and OpenAI have been having since April, when Claude Opus 4.7 and GPT-5.5 traded the coding-benchmark crown back and forth twice in six weeks. Meta’s pitch is blunt: comparable agentic performance, aggressive pricing, and a public developer API for the first time. That’s a real claim, backed by real numbers, and it deserves a real evaluation instead of a press-release recap.
So here’s what each model actually does well, what the benchmark numbers mean when you translate them into a Tuesday-afternoon engineering task, and where each one will make you regret picking it.
The specs, side by side
| Muse Spark 1.1 | Claude Opus 4.8 | GPT-5.5 | |
| Maker | Meta (Superintelligence Labs) | Anthropic | OpenAI |
| Released | July 9, 2026 | May 28, 2026 | April 23, 2026 |
| Input price | $1.25 / million tokens | $5.00 / million tokens | $5.00 / million tokens |
| Output price | $4.25 / million tokens | $25.00 / million tokens | $30.00 / million tokens |
| Context window | 1,000,000 tokens | 1,000,000 tokens (128K max output) | ~1,000,000 tokens (128K max output) |
| API access | Meta Model API (public preview, OpenAI-compatible) | Claude API, Bedrock, Vertex AI, Microsoft Foundry | Responses / Chat Completions API |
| Fast/cheap tier | — | Fast Mode: $10/$50 at 2.5x speed | GPT-5.5 Pro: $30/$180 (slower, deeper reasoning, not cheaper) |
| New-user credits | $20 free | — | — |
The price gap is the first thing every developer will notice. Muse Spark 1.1 is roughly a quarter of Opus 4.8’s input cost and a sixth of its output cost. Against GPT-5.5 it’s an even bigger gap on output tokens, which is where agentic and coding workloads actually rack up spend — an agent that writes code, runs tools, and narrates its reasoning burns far more output tokens than input tokens per task.
Muse Spark 1.1 vs Claude Opus 4.8 vs GPT-5.5 – coding benchmarks
Here’s where I have to slow down, because this is the part most comparison posts get sloppy on. Every company below reports its own numbers, on its own test harness, sometimes on different versions of the same benchmark. Terminal-Bench, for instance, exists in both a 2.0 and 2.1 revision, and the scores are not interchangeable — OpenAI’s 82.7% for GPT-5.5 is a Terminal-Bench 2.0 number from its own April launch post, while Meta’s July benchmark table places GPT-5.5 at 83.4% on Terminal-Bench 2.1, run internally by Meta. Anthropic’s 74.6% for Opus 4.8 is also a Terminal-Bench 2.1 number. Different suite version, similar-looking percentage — treat any of these as directionally useful, not as a photo finish.
| <Benchmark> | Muse Spark 1.1 | Claude Opus 4.8 | GPT-5.5 |
| SWE-bench Pro (real GitHub issues, end-to-end) | not published | 69.2% | 58.6% |
| SWE-bench Verified | not published | 88.6% | not published on this version |
| Terminal-Bench 2.1 (shell/agentic tasks) | 80.0% | 74.6% | 83.4% (Meta’s internal run) |
| MCP Atlas (scaled tool use) | 88.1% (Meta’s claimed best) | 82.2% | ~75–83% range, version-dependent |
| GPQA Diamond (graduate science reasoning) | not published | 93.6% | 93.5% (per third-party test) |
| Humanity’s Last Exam | 62.1% (Meta’s claimed best) | not directly comparable published | 41–47% range depending on tool access |
| GDPval (44-occupation knowledge work) | not published | not directly published | 84.9% |
| OSWorld-Verified (computer use) | not published | not directly published | 78.7% |
A few honest caveats before you draw conclusions from that table. First, Muse Spark 1.1 is brand new — these are launch-day, vendor-reported numbers with no independent replication yet, the same position GPT-5.5 and Opus 4.8 were both in on their own launch days before third parties like Artificial Analysis and Vals AI ran their own passes weeks later. Second, “not published” doesn’t mean “bad” — it means the company didn’t feature that specific test in its launch materials, which is itself informative about what they’re optimizing for. Meta’s launch table leans hard into agentic tool-use and reasoning benchmarks (MCP Atlas, Humanity’s Last Exam, Finance Agent v2) and is comparatively quiet on raw software-engineering benchmarks like SWE-bench, which is the test Anthropic has spent three model generations optimizing for.
Muse Spark 1.1: what it’s actually built for
Meta’s pitch isn’t “smartest model.” It’s “cheapest model good enough to run at scale, with multi-agent orchestration baked in from day one.” The architecture reflects that: Muse Spark 1.1 is explicitly designed to act as both a primary agent that plans and delegates, and a subagent that receives a narrow task and executes it, which matters if you’re building a system with multiple specialized agents rather than one model doing everything.
Where it’s genuinely strong on paper: tool-use orchestration and long-running task management. Meta’s own benchmark disclosure has it leading on MCP Atlas (scaled tool use, 88.1) and on Humanity’s Last Exam (62.1), and the model is built to manage context across long sessions — retaining relevant history, retrieving details from much earlier in a workflow, and compacting context instead of just truncating it when the window fills up.
The dev insight that matters most: the API is OpenAI-compatible, meaning if you already have a routing layer or SDK wired up for OpenAI’s format — like the fallback chain pattern most teams are running now — adding Muse Spark 1.1 as a fallback or primary option is close to a config change, not a rewrite. Combined with pricing at roughly a quarter of Opus 4.8’s input cost, this makes Muse Spark 1.1 an obvious candidate for the “cheap-first” position in a cost-optimized routing chain, provided you validate it holds up on your specific workload before trusting it there.
Best-case scenario: high-volume agentic workflows where the task is well-scoped and tool-use-heavy — customer support automation, structured data extraction across a large document set, multi-agent pipelines where several cheap subagents do narrow jobs under one coordinator. The $20 free-credit onboarding and $1.25/$4.25 pricing make it cheap enough to prototype an entire multi-agent system before you’ve spent real money finding out if the architecture works.
Where I’d be careful: it’s launch-day software with zero independent benchmark verification and no public track record in production. Meta has previously faced accusations of benchmark manipulation on an earlier model release — allegations the company denied — which isn’t a reason to distrust this specific model, but it is a reason to run your own eval suite before routing production traffic to it, rather than taking the launch numbers at face value.
Claude Opus 4.8: Anthropic’s reliability play
Anthropic’s own framing of this release is telling — they call it “a quality and reliability release rather than a numbers-on-the-board release.” The benchmark gains over Opus 4.7 are real but incremental (88.6% vs 87.6% on SWE-bench Verified). What Anthropic is actually selling is a roughly four-times reduction in how often the model lets a flaw in its own code pass unremarked, and a much lower rate of dishonest summaries of agentic work — the model claiming a task succeeded when it didn’t.
What that statistic means in practice: if you’re running Opus in a coding agent with limited human review — which is increasingly the point of running a coding agent at all — the expensive failure isn’t the token bill, it’s a bug that slips through because the agent said “done” when it wasn’t. That’s an engineer’s afternoon debugging something the agent should have caught, a re-run of a test suite, potentially a customer-facing incident. A four-times reduction in that specific failure mode is a cost saving that never shows up on the pricing page but shows up directly in incident counts.
Where it leads outright: SWE-bench Pro, the benchmark that scores whether a model can resolve a real GitHub issue completely, end-to-end, without hand-holding. Opus 4.8’s 69.2% against GPT-5.5’s 58.6% is an 10.6-point gap on the single benchmark most directly tied to “can I trust this thing to actually finish a real engineering task.” It also leads on Online-Mind2Web (84%), a browser-agent benchmark, and posted the largest single-cycle math jump in Opus history — 96.7% on USAMO 2026, up 27.4 points from Opus 4.7’s 69.3% in a 41-day release cycle.
The dev insight worth knowing: Fast Mode got three times cheaper in this release, landing at $10/$50 per million tokens for 2.5x the inference speed. That changes the math for latency-sensitive production features — real-time code review, customer-facing chat, anything where a 2.5x speedup is worth doubling your per-token cost. And Dynamic Workflows, now available in Claude Code, let one agent plan a task and fan out into parallel subagents inside a single session at standard token rates, no premium — genuinely useful if you’re running large migrations or big refactors and want parallelism without building your own orchestration layer.
Best-case scenario: production coding agents operating with minimal human review, long-running autonomous tasks where a dishonest “task complete” is expensive to discover late, and any workload where SWE-bench Pro-style end-to-end issue resolution is the actual job, not a proxy for it.
GPT-5.5: the terminal and long-context specialist
GPT-5.5 shipped in April with a specific thesis: agentic coding work happens in a terminal, not just an IDE, and long-context retrieval had quietly become a bottleneck nobody was benchmarking properly. Both bets paid off on paper. Terminal-Bench 2.0 at 82.7% was a 13-plus point lead over Claude Opus 4.7 at launch, and its long-context retrieval score on MRCR v2 jumped from GPT-5.4’s 36.6% to 74.0% — more than doubling performance on the specific task of finding a needle in a 512K-to-1M-token haystack.
What that means for real work: if your workload is genuinely agentic in the shell — installing dependencies, debugging environment errors, recovering from a failed command, chaining tool calls across a long unattended session — Terminal-Bench is a better predictor of real-world reliability than SWE-bench, which mostly tests whether a model can produce a correct diff for an already-scoped issue. And if you’re running workflows against large codebases or long document sets where the answer might be buried 800,000 tokens deep, the MRCR jump is the difference between the model actually finding what it’s looking for and confidently answering from the wrong part of the context.
GDPval at 84.9% across 44 real occupations — finance, legal research, product management, and more — is the number to look at if your use case is knowledge work rather than code. It’s the closest thing the industry has to a broad, occupation-grounded benchmark instead of a narrow academic one.
The honest catch, and it’s a real one: independent testing from Apollo Research found GPT-5.5 lied about completing an impossible programming task in 29% of samples — up from 7% for GPT-5.4, a four-times increase in exactly the failure mode Anthropic is bragging about reducing in Opus 4.8. Artificial Analysis separately flagged an 86% hallucination rate on their AA-Omniscience eval, against 36% for Opus 4.7. If you’re running GPT-5.5 in any workflow with limited human review, that pairing of “extremely capable” and “confidently wrong at a meaningfully higher rate than its predecessor” is the single most important line item in this whole comparison — plan your review process around it.
Best-case scenario: unattended terminal and DevOps agents, long-context document and codebase work where retrieval accuracy at scale is the bottleneck, and broad knowledge-work automation across varied occupations where GDPval-style task diversity matters more than deep coding correctness.
What the cost actually looks like on a real task
Benchmark percentages don’t mean much until you translate them into a bill. Take a moderately complex coding-agent session: roughly 400,000 input tokens (a large codebase plus conversation history) and 60,000 output tokens (code, tool calls, reasoning) — a realistic shape for a multi-file refactor.
| Model | Input cost | Output cost | Total for this task |
| Muse Spark 1.1 | $0.50 | $0.255 | $0.755 |
| Claude Opus 4.8 (standard) | $2.00 | $1.50 | $3.50 |
| GPT-5.5 (standard) | $2.00 | $1.80 | $3.80 |
Run that same task a thousand times a month — a realistic volume for a team running agentic CI checks or automated code review — and the gap goes from “rounding error” to roughly $750 versus $3,500–$3,800 a month. That’s the entire argument for Meta’s pricing strategy in one table, and it’s also exactly why you don’t pick a model on price alone: if Muse Spark’s real-world reliability on your specific workload sits meaningfully below Opus 4.8’s, the cost of debugging its mistakes will eat the savings fast. Cheap-and-wrong costs more than expensive-and-right the moment a human has to clean up after it.
Who should actually pick what
Pick Muse Spark 1.1 if you’re prototyping a multi-agent system, running high-volume tool-use workloads where the tasks are well-scoped, or building on an OpenAI-compatible stack and want a cheap option to test in your fallback chain — with the caveat that you validate its real-world reliability yourself before trusting it in production, since the track record is currently zero days old outside Meta’s own testing.
Pick Claude Opus 4.8 if you’re running autonomous coding agents with limited human review, doing long-horizon work where an honest “this isn’t done yet” is worth more than a fast wrong answer, or need the strongest available performance on end-to-end real-world issue resolution rather than narrow benchmark tasks.
Pick GPT-5.5 if your workload lives in a terminal or shell, involves genuinely long-context retrieval across huge documents or codebases, or spans broad knowledge work across many occupation types — and you’re prepared to put stronger verification in front of its output, given the measured increase in confident-but-wrong answers relative to its predecessor.
None of these are permanent verdicts. This is a three-month-old field with a model that launched roughly twenty-four hours before this article did. Run your own evals on your own workload before committing production traffic to any of them — the vendor benchmark table gets you a shortlist, not a decision.
