Our models hold their own. Astraeus 3, the premium route, scores 84.6 on TerminalBench 2.1 — ahead of GPT-5.5 and every premium peer shown, just behind GPT-5.6 Sol — with a comparable result on SWE-bench Verified. We publish the named rows with their sources, and we do not invent Claude or Gemini head-to-head figures we have not measured.
Sigilix vs Claude
A Claude alternative for org-aware work.
Claude is a general-purpose assistant. Sigilix is an engineering platform with its own model line — frontier-competitive at the premium tier — grounded in a memory index built from your organization.
The comparison is not about which model family is smarter in the abstract. It is about where the context comes from. A general assistant starts each task with whatever you bring to it. Sigilix starts with what your team has already established — across GitHub, Linear, Slack, the CLI, and every past review.
How to read this comparison
Where Sigilix wins — and where it doesn't.
Where our premium model lands on public benchmarks — and what the platform builds on top of it.
These comparisons are not decided by raw model score. Sigilix grounds every model in your org and developer memory index and runs the whole workflow as one system — review, triage, CLI, chat, and the Slack assistant. The output gets more tailored to your codebase and cheaper per task as the index grows. That is the edge.
A frontier assistant is a fine default for open-ended general work, and many Sigilix users keep one. When the work is your codebase and your team's workflow — review that proves its findings, tickets that need owners, answers that already know how your systems fit together — Sigilix is built for exactly that.
The category
Different shapes of tool.
Claude is a general-purpose AI assistant and model family built by Anthropic, widely used for coding through chat, APIs, editors, and terminal tooling. As general-purpose models, the frontier tiers are excellent, and nothing on this page claims otherwise.
Sigilix is a different shape of product: an engineering platform that runs its own model line — Boreas live today, with Pyroeis, Astraeus, and Phanes as the tier story — and embeds those models in the places engineering work already happens. Reviews post inline on GitHub pull requests. Triage rewrites Linear and Jira tickets. The assistant answers in Slack. The CLI works in your terminal, and chat runs in the browser.
The load-bearing difference is the memory index. Everything your team does on those surfaces feeds one index, kept per organization and per developer. Sigilix models are routed through that index, so a competitive model arrives at your task already carrying your repository shape, your conventions, and your team's prior decisions.
Leverage to tailor
How the grounding works.
The pipeline is simple to state: pull your org's real context, route the model tier the task needs, write what was learned back to the index.
01Leverage
Sigilix pulls your repositories, tickets, review history, and team conversations as context — the material a general assistant only sees when someone pastes it in.
02Tailor
Routing picks the model tier the task needs and shapes the run with your org's memory: conventions, prior decisions, known failure patterns.
03Compound
Every task writes back to the index. The platform becomes more org-aware with use, which is a property of the system, not of any single model's weights.
Side by side
Assistant vs platform.
The left column describes the general-purpose assistant category. The comparison is about system shape, not a claim about any vendor's roadmap.
| Dimension | General-purpose assistant | Sigilix |
|---|---|---|
| What it is | A general-purpose assistant and model family, used for coding through chat, APIs, editors, and terminal tooling. | An org-aware engineering platform: review, triage, CLI, Slack assistant, and browser chat, with its own model line underneath. |
| Memory | Context and memory live inside the assistant's own surfaces. | One memory index per organization and per developer, fed by GitHub, Linear, Slack, CLI, and review — read by every surface. |
| Codebase context | Assembled per project or per session by the user and the tooling around the model. | Inherited from the index: repository shape, conventions, prior decisions, dismissed findings, active constraints. |
| Model choice | You choose a model tier per conversation or configuration. | Routing chooses the tier per task — Boreas today; Pyroeis, Astraeus, and Phanes as the tier story — tailored by your context. |
| Workflow integration | The assistant is a destination; your PRs, tickets, and channels connect to it through separate setups. | The platform lives where the work is: inline PR reviews on GitHub, triage inside Linear and Jira, answers inside Slack. |
| Token cost over time | Scales with the context each session carries. | Falls over time: memory replaces re-sent context, so the same task costs fewer tokens as the index grows. |
| Model quality | Frontier general-purpose models. | The premium route, Astraeus 3, is frontier-competitive on public benchmarks (84.6 on TerminalBench 2.1); every tier is grounded in org and developer memory the model can actually use. |
Benchmarks
Where Astraeus actually lands.
This is our premium route, Astraeus 3, against named base and frontier peers on the public benchmarks. On TerminalBench 2.1 it lands at 84.6 — ahead of GPT-5.5 and every premium peer shown, just behind GPT-5.6 Sol. On SWE-bench Verified it lands just behind one premium peer and ahead of GPT-5.5. Peer rows are public references; we do not invent head-to-head numbers we have not measured.
Benchmark frame
Astraeus benchmark frame
Astraeus 3 is shown as our premium model, with the displayed Astraeus task rows carrying the 3.6-point Sigilix context lift. Peer rows use the public benchmark references.
These charts separate the questions that matter: command-line workflow performance, repository repair performance, maximum output room, and native context capacity. Missing public rows are omitted instead of being inferred.
TerminalBench
TerminalBench is the closest public frame for terminal-bound coding work: reading the repository, choosing commands, reacting to execution feedback, and making progress through a task where each step depends on the one before it.
Astraeus is built for the longer version of that workflow. It is the model you reach for when the answer needs to connect repo structure, prior reviews, issue history, and current evidence before proposing the next move.
The Astraeus row applies the 3.6-point Sigilix context lift to the reference TerminalBench row. Separate ultra or agentic mode rows are omitted so the comparison stays focused on base model rows that fit this route.
Values shown: GPT-5.6 Sol, Astraeus 3 with context lift, GPT-5.5, Claude Opus 4.8, Kimi K2.7 Code, Qwen3.7-Max, and NVIDIA Nemotron 3 Ultra.SWE-bench Verified
SWE-bench Verified shifts the comparison from terminal flow to repository repair. The task is to locate the relevant code, understand the failure, make the patch, and preserve the behavior the tests are meant to protect.
Astraeus is designed for that kind of broad repair context. Its benchmark row is useful, but the platform around it matters because your repository memory and prior decisions help keep the repair grounded in how your codebase actually works.
The Astraeus row applies the same 3.6-point context lift to the reference SWE-bench Verified value. The chart only includes peers with a public SWE-bench Verified value; GPT-5.6 Sol and Terra are omitted here because the reference set did not include public SWE-bench Verified rows for them.
Values shown use public SWE-bench Verified rows for Claude Opus 4.8, Astraeus 3 with context lift, GPT-5.5, Qwen3.7-Max, Kimi K2.7 Code, and NVIDIA Nemotron 3 Ultra.SWE-bench by output effort
This chart shows Astraeus as an effort curve instead of a single detached benchmark point. The rightmost Astraeus point carries the 3.6-point context lift on top of the reference SWE-bench row; earlier points show the working shape for shorter and medium-length repair work.
Astraeus also applies the tested 63% repeated-context reduction at each effort point. The raw output budget still exists, but the chart plots the effective load after memory carries forward the context that would otherwise be repeated.
Peer lines are capacity checkpoints anchored to their public SWE-bench Verified rows. They give visual context for output room and score without pretending every vendor publishes the same per-effort benchmark series.
Hover a dot to see the effort label, effective output tokens, raw output budget when applicable, context window, and score used for that checkpoint.
Scores are anchored to public SWE-bench Verified rows for Astraeus 3, Claude Opus 4.8, GPT-5.5, Qwen3.7-Max, and Kimi K2.7 Code. Astraeus effective tokens use the tested 63% repeated-context reduction.Astraeus 34 effort points · 63% repeated-context reductionClaude Opus 4.82 effort points · 128,000 max output tokensGPT-5.52 effort points · 128,000 max output tokensQwen3.7-Max2 effort points · 65,536 max output tokensKimi K2.7 Code3 effort points · 262,144 max output tokens
Max output capacity
Output capacity is not the same as intelligence. It is the room the route can use when the answer needs to carry planning, code edits, evidence, and follow-up reasoning without being cut short.
For Astraeus, that room matters because premium work often spans a larger surface: multiple files, earlier PR decisions, issue constraints, and a repair plan that has to remain coherent across the whole change.
The chart uses only rows with public max-output limits. Models without a public output-token cap in the reference set are omitted rather than estimated.
Values shown use max-output rows for Kimi K2.7 Code, Astraeus 3, Claude Opus 4.8, GPT-5.5, and Qwen3.7-Max.Context capacity
Context capacity is the surface a route can hold in the current run. Memory is different: it is what Sigilix can carry forward so the same background does not have to be reconstructed every time.
Astraeus sits at the intersection of both. The native window gives the model room to read the current work; memory gives the product continuity across PRs, Slack decisions, Linear issues, triage runs, and CLI sessions.
The chart uses the public context-window rows and leaves out models where the reference set did not provide a public context value.
Values shown use context-window rows for GPT-5.5, Astraeus 3, Claude Opus 4.8, NVIDIA Nemotron 3 Ultra, Qwen3.7-Max, and Kimi K2.7 Code.The honest trade
When each is the right choice.
If you want a general-purpose assistant for open-ended work across writing, analysis, and code, a frontier assistant is a reasonable default, and many Sigilix users keep one.
Sigilix is the alternative when the work is your codebase and your team's workflow: pull-request review that has to prove its findings, tickets that need grounding and owners, terminal and Slack help that should already know how your systems fit together. For that work, org and developer memory does what raw model strength cannot — it makes the answer specific to you, and it makes each task cheaper than the last.
The full argument for why memory outperforms a bigger context window — and what that does to token spend — is on the memory page.
FAQ
Common questions.
Is Sigilix an alternative to Claude?
For codebase-aware engineering work, yes. Sigilix is not a general-purpose assistant; it is an org-aware platform where review, triage, CLI, Slack, and chat share one memory index, with Sigilix's own models underneath. If your goal is engineering work grounded in your organization's context, that is the job Sigilix is built for.
Are Sigilix models as good as Claude?
Our premium route, Astraeus 3, is frontier-competitive on public benchmarks — 84.6 on TerminalBench 2.1, ahead of GPT-5.5 and every premium peer shown and just behind GPT-5.6 Sol, with a comparable SWE-bench Verified result. We publish the named rows with their sources and do not invent Claude head-to-head numbers we have not measured. On top of a strong model, the compounding advantage is grounding in your org and developer memory, so it does more of your specific work than a model starting from a blank context.
Which Sigilix models are available today?
Boreas, the flash route for quick review, CLI help, and small code changes, is live. Pyroeis (base), Astraeus (premium, repository-scale reasoning), and Phanes (frontier) are the rest of the tier story and are published as they clear release criteria.
Why route models through a memory index instead of using a bigger context window?
A bigger window lets you pay to re-send more context every time. A memory index means the context is already there: the org and developer layers carry repository shape, conventions, and decisions into each task, which cuts repeated token spend and keeps answers consistent with how your team actually works.
Models that hold their own, grounded in the one thing no general assistant has: your organization's memory.