Claude Sonnet 5.5 vs Muse Glimmer: A 30B Laptop Model Against the New Sonnet

The framing question for this page is whether the comparison is legitimate at all, and the honest answer is that Claude Sonnet 5.5 and Muse Glimmer are not competitors in the ordinary sense. On Artificial Analysis's Intelligence Index, revision v4.3.2, Claude Sonnet 5.5 scores 56 and Muse Glimmer scores 17, and that gap is not noise — it is roughly the distance between a frontier general-purpose model and a very good small one. What makes the pairing worth reading anyway is that Muse Glimmer has one property the other cannot buy at any price: it is a 30-billion-parameter open-weight model, published by M​eta on August 10, 2026 under Apache 2.0, quantized to 4-bit so it fits under 20 GB of VRAM, and designed to run with the network unplugged. Claude Sonnet 5.5 arrived on September 28, 2026 as the vendor's fastest-and-smartest Sonnet, priced at $2.00 per million input and $10.00 per million output, and reachable only over a network. The real decision is not which model is better. It is where you want the tokens to run.

Two models, two definitions of the job

Muse Glimmer comes out of M​eta Superintelligence Labs as a 30B-parameter model trained by logit distillation from M​eta's larger Muse Spark teacher, then fine-tuned on long-context agentic data and reinforced for tool use. M​eta shipped it already quantized: 4-bit takes the memory footprint from over 55 GB at full precision to under 20 GB, which is what puts it inside a 24 GB or 32 GB consumer GPU envelope. M​eta tested it on an Nvidia RTX 5090 and on Apple M4 Max and M5 Max silicon. It takes text and image input, returns text, runs a 131,072-token context window that M​eta says can be extended to 262,144, and carries a January 2026 knowledge cutoff.

Claude Sonnet 5.5 is the second model in A​nthropic's 5.5 generation and the one the company positions as the best combination of speed and intelligence in its lineup. It runs a 1,000,000-token window, up to 128,000 output tokens synchronously and 300,000 on the Message Batches API behind the output-300k-2026-03-24 header, accepts text, images and files, offers adaptive thinking at selectable effort with a June 2026 cutoff, and is available with zero data retention from launch. Storage is $0.20 per million tokens on cache reads and $2.50 per million on cache writes. A​nthropic says it runs 30% faster than Claude Sonnet 5 and costs up to 30% less per task at unchanged rates — vendor claims, not independently reproduced.

The spec contrast is worth seeing in one pass, because almost every line is a different kind of thing rather than a better or worse one:

• Weights — Muse Glimmer Apache 2.0, downloadable, quantized to 4-bit vs Claude Sonnet 5.5 closed

• Where it runs — Muse Glimmer on your own GPU, offline vs Claude Sonnet 5.5 on A​nthropic infrastructure

• Parameters — Muse Glimmer 30B total, under 20 GB at 4-bit vs Claude Sonnet 5.5 undisclosed

• Context — Muse Glimmer 131,072 tokens, extendable to 262,144 vs Claude Sonnet 5.5 1,000,000 tokens

• Price per million — Muse Glimmer no per-token charge, your hardware vs Claude Sonnet 5.5 $2.00 in / $10.00 out

• Intelligence Index v4.3.2 — Muse Glimmer 17 vs Claude Sonnet 5.5 56

• Cost per Index task as measured — Muse Glimmer $0.06 vs Claude Sonnet 5.5 $7.60

Two figures on that list deserve unpacking, because both are traps. The first is the $0.06 per-task number: it is what Artificial Analysis spent calling Muse Glimmer (high) through a hosted endpoint at $0.325 per million input and $1.35 per million output, so it measures the economics of renting this model, not of running it. Self-hosted, the marginal per-token cost goes away and is replaced by a GPU you already own or have to buy. The second is the Index gap itself — a 30B model quantized into 20 GB is being scored on the same ruler as frontier models, and the board says as much when it places Muse Glimmer in the top handful of open-weight models while noting it is expensive for its size.

Where Muse Glimmer is genuinely good, and where the wheels come off

The composite score is much too blunt to be the whole answer, because Muse Glimmer is not uniformly seventeen. It is fast — Artificial Analysis measures 143.8 output tokens per second, ahead of Claude Sonnet 5.5's 138.7 — and it is concise, generating 59M tokens across the Index evaluation against Claude Sonnet 5.5's 410M. And on one evaluation it is close to the frontier: long-context recall, where it scores 83.3% against Claude Sonnet 5.5's 82.7%. A 30B model matching a brand-new frontier Sonnet on long-context retrieval is the most surprising line on this page, and it is consistent with the way M​eta trained it — long-context agentic data, distillation from a much larger teacher.

The agentic results are where the model's ceiling shows and the ranking stops being flattering. On Terminal-Bench 4.0 Muse Glimmer scores 0.5% against Claude Sonnet 5.5's 63.6%. Its automation-benchmark score is 0.068 against 0.713. Humanity's Last Exam: 22.0% against 55.0%. Output of a scale that small on an execution benchmark means the model is not completing tasks, and no local-hosting saving makes a task that never finishes cheaper.

The research literature is blunt about this too, and it is worth stating rather than burying: a peer-reviewed multi-agent orchestration study, in which Muse-Glimmer-30B was one of the models tested, found it recovered none of its candidates. M​eta's own benchmark table for the model is a vendor document and is labelled as such here; the Artificial Analysis figures above are independent measurements, and they are the ones this piece leans on.

So the split is legible. Muse Glimmer is strong for its size at reading a long input and answering about it, fast, cheap to run, and it fits in a laptop-class GPU. It is not a model you hand a multi-step software task and walk away from. That is the honest boundary, and a comparison written around composite scores alone would hide it.

What you are actually buying at the frontier rate

Claude Sonnet 5.5's premium buys capability first, and the seventeen-point Index gap is the headline form of it. But three other things come with the $10.00 output rate that do not appear on any leaderboard.

The first is the window. A million tokens is roughly seven and a half times Muse Glimmer's 131,072, and A​nthropic charges the standard rate across all of it, with cache reads at a tenth of the input price so that carrying a large context across many calls is affordable rather than theoretical. A repository-scale prompt does not fit in the smaller window at all, so this is a capability difference and not a preference.

The second is tool fidelity. Five behaviours changed between Claude Sonnet 5 and Claude Sonnet 5.5, and all five are about tool use and reasoning: non-default sampling parameters now return a 400, thinking: {"type": "disabled"} now returns a 400 and becomes between_tools, forced tool choice of "any" or a named tool is rejected so loops must move to auto with strict tool use, the older computer_20251124 tool is refused on the Claude API and Goo​gle Cloud, and thinking blocks are now bound to the producing model and account. A sixth change fails nothing but goes quiet: text between tool calls returns inside thinking blocks, so a streaming application stops showing output until it sets a display value or turns off up-front thinking. That is a day of migration work for anyone on Sonnet 5, and it is the price of admission to the agentic reliability the benchmark rows above are measuring.

The third is provenance and data handling. Zero data retention is available from launch, A​nthropic publishes a retirement floor of September 28, 2027, and the model is available on the Claude API, Bedrock, Goo​gle Cloud and Microsoft Foundry. For a regulated buyer, those are procurement facts that decide the purchase before the benchmark does.

Offline is a requirement, not a cost saving

The reason this pairing belongs on one page is that for a specific class of deployment, Muse Glimmer is not a cheaper option — it is the only option. A request that cannot leave the device, a factory floor or a field site with no connectivity, an air-gapped environment where an API call is a compliance violation, or a latency budget where a round trip is already too much: none of those are solved by a better model behind a network. Apache 2.0 weights that fit under 20 GB and run offline are the entire answer, and Claude Sonnet 5.5 at any price does not participate in the question.

M​eta's published testing on RTX 5090 and Apple M4 Max and M5 Max silicon is the practical reference for what "runs locally" means here, and the 4-bit quantization is what makes those targets reachable at all — the full-precision footprint is over 55 GB. Anyone planning a deployment should treat the hardware figures as M​eta's rather than measured here.

For everyone else, the two models are complements rather than substitutes, and the operationally awkward part is that holding an Anthro​pic API model and a self-hosted M​eta model normally means two entirely separate stacks. OrcaRouter puts 200-plus models behind one Open​AI-compatible endpoint with provider list price passed through and no markup added, automatic failover between upstream providers and a routing DSL for composing calls — which covers the hosted half of that arrangement, and M​eta's larger Muse Spark 1.2 teacher is routable here as meta/muse-spark-1.2 at $1.25 input and $4.25 output per million tokens across a 1,048,576-token window, with $0.15 cache reads. It is carrying 42.8 million tokens of traffic a week through this catalogue, which is the useful part of the arrangement: the hosted teacher is on the same key as everything else, and the model you run locally never has to touch a network at all.

Three honesty notes, because they are easy to get wrong. Muse Glimmer itself is not one of our routes — the model card does not exist in our catalogue, and this page says so rather than implying otherwise. The capture below is the page for the model that does exist, Muse Spark 1.2, which is the checkpoint Muse Glimmer was distilled from rather than Muse Glimmer itself. Claude Sonnet 5.5 is not in our catalogue either, one day after release; the route to it is A​nthropic's own API, and Claude Sonnet 5 has been routable here since June 30 at $2.00 and $10.00 for anyone who wants the staging target.

How to decide

Start from the constraint, not the score. If the request cannot leave a machine or a network, Muse Glimmer is the answer and the Index gap is irrelevant — a 30B Apache 2.0 model that runs offline and matches a frontier model on long-context recall is, for that job, the best thing available. If the request has to complete a multi-step software task, a 30B model that scores half a percent on Terminal-Bench is not in the running regardless of the hardware you already own.

Between those two poles, the tiebreaker is measurement rather than opinion: tokens per completed task on your own workload, priced twice — once at Claude Sonnet 5.5's $2.00 and $10.00, and once at the amortized cost of the GPU. The one number that reframes the whole comparison, $0.06 per Index task against $7.60, is a hosted-rental figure for a model most people will run themselves, and quoting it as if it were a like-for-like saving would be the easy mistake this page exists to avoid.