Shannon 3.1

Most model releases ask you to take a capability claim on faith and wait for a benchmark table to settle the argument. This one is easier to check: open two tabs, put the same prompt into shannon-3 and shannon-3.1, and watch. The difference in how fast the answer arrives is not subtle, and it is not a rendering trick. Shannon 3.0 was throttled on purpose. Shannon 3.1 is not.

01What actually changed in Shannon 3.1

Shannon 3.0 introduced the thing that defines this family: an iterative reasoning loop. The model does not answer on its first thought. It thinks, drafts, reviews its own draft against the question, and improves it. Lite runs one pass of that loop; Pro runs the full loop, including a knowledge harvest step before it drafts. That design is described in detail in the Shannon 3 research article, and none of it has changed in 3.1.

What changed is the machinery underneath. Shannon 3.0 was served through a third-party inference host — a sensible way to launch a model, and a constraining way to run one. You inherit somebody else's serving configuration, somebody else's queue, somebody else's context ceiling, and somebody else's idea of how many tokens per second you are allowed to have. Shannon 3.1 runs on our own GPU cluster, on a serving stack we configure, and every headline number in this article is downstream of that single decision.

	Shannon 3.0	Shannon 3.1
Where it runs	Third-party host	Our own GPU cluster
Context window	32,768	196,608
Output streaming	Paced / rate-limited	Full engine speed
Weights	Host default	4-bit NVFP4
Decoding	Standard	Speculative
Reasoning loop	Think → draft → review → improve	Unchanged
Refusal layer	None	None
Model ids	shannon-3, shannon-3-pro	shannon-3.1, shannon-3.1-pro

02Why Shannon 3.1 feels 10-15x faster

The speed number is the one people query first, so it is worth being precise about where it comes from. There are two independent contributions, and the larger one is the less glamorous of the two.

We removed the speed gate

Shannon 3.0's output passed through a pacing layer. Tokens left the engine, were held, and were released to your connection on a schedule. This was not an accident or a bug. When a shared inference host is the bottleneck, pacing output smooths the load, keeps a long generation from monopolizing a slot, and makes the stream arrive at a predictable, readable rhythm rather than in bursts. It is a defensible engineering choice, and it cost every user real wall-clock time on every single reply.

Shannon 3.1 has no speed gate and no reveal pacing. Tokens are written to your stream as the engine produces them. If the engine is generating quickly, you see it generating quickly. There is no smoothing buffer between the model and your terminal, your chat window, or your SSE reader. For a long answer — a 2,000-token analysis, a file of generated code — this alone accounts for the majority of the improvement you will notice.

An honest consequence: the stream is now bursty. Speculative decoding (below) emits accepted tokens in short runs, so text can arrive in visible chunks rather than at a metronomic one-word-at-a-time cadence. If you built UI around 3.0's smooth cadence, it will still work — the token order and content are unaffected — but you may want to reintroduce your own client-side easing if you preferred the typewriter look. We think most people would rather have the seconds back.

The engine itself got faster

Removing the gate only helps if what is behind the gate is quick. The second contribution is the serving stack described in section 03: 4-bit NVFP4 weights and speculative decoding on current-generation, FP4-native accelerators in our own cluster. Together these raise the ceiling that removing the gate exposes.

Relative end-to-end reply latency, Shannon Lab internal evaluation, September 2026. The range reflects prompt length and tier: short prompts on Lite sit near the low end, long generations on Pro near the high end.

03What speculative decoding actually does

"Speculative decoding inference" gets used as a marketing word, so here is the mechanism, plainly.

A language model normally produces one token per forward pass. That pass is dominated not by arithmetic but by memory bandwidth — the weights have to be read to compute anything, and reading them takes far longer than the math does. The GPU spends most of its time waiting on memory with its compute units idle. Generating 500 tokens means paying that latency 500 times, sequentially.

Speculative decoding attacks the sequential part. A small, cheap draft model proposes a short run of likely next tokens — say four or eight. The main model then evaluates that whole run in a single batched forward pass, which costs barely more than evaluating one token would have, because the expensive part (reading the weights) happens once either way. Every proposed token the main model agrees with is accepted and emitted immediately. At the first disagreement, the run is truncated and normal decoding resumes from there.

The acceptance rate does the work. On predictable text — boilerplate, code structure, the connective tissue of an argument — the draft model guesses well and long runs land at once. On genuinely hard tokens, acceptance falls and the system degrades gracefully back to ordinary one-token-at-a-time decoding. That asymmetry is the one you want: it accelerates the easy parts and does not touch the hard ones.

Why 4-bit weights belong in the same paragraph

Because the bottleneck is memory bandwidth, shrinking the weights is a direct speed lever, not just a memory-footprint one. NVFP4 is a 4-bit floating-point format with fine-grained per-block scaling, which is what lets it hold accuracy where older 4-bit integer quantization schemes lost it. Roughly a quarter of the bytes to read per forward pass means proportionally less time waiting on memory, and it leaves far more headroom for the long-context KV cache that a 196K window demands.

The two compound rather than merely add: fewer bytes per pass, and fewer passes per token emitted. That is what makes ungated, full-speed streaming affordable to serve rather than a cost we would have to claw back through pacing — which is precisely what the pacing layer was doing on 3.0.

04196,608 tokens: what a 6x window unlocks

Shannon 3.0 had a 32,768-token window: a working session, not a document. Roughly 70-80 pages of prose, minus whatever the reasoning loop consumes for its own thinking, minus your system prompt, minus the conversation so far. Real work hit that wall constantly, and the workarounds — chunking, summarizing, retrieval over your own material — all degrade the thing you were trying to preserve. 196,608 tokens is a different category of problem.

Concretely, that is on the order of 400-500 pages of English text, or a mid-sized codebase with its tests and its README, or a year of one project's meeting notes, or a full contract set with all its exhibits — held in one conversation, addressable in one question, with no chunking layer between you and the material.

• Whole-repository questions. Load the code and ask why a bug reaches production, rather than pasting the three files you already suspected. The model can find the file you did not think to include.
• Long-document analysis without retrieval. Retrieval is a lossy pre-filter that decides what the model is allowed to see. At 196K you can often skip it and let the model read everything, which removes an entire class of failure where the right passage was never retrieved.
• Conversations that stay coherent. A long working session no longer silently drops its own beginning. Constraints you set in message three still apply in message eighty.
• Room for the reasoning loop. The loop's own thinking, drafting and review consume context. On 3.0 those steps competed with your material for a scarce budget. On 3.1 they fit comfortably, which is part of why the quality gain and the window increase arrived together.
• Large mixed inputs. Images, extracted document text and code in a single turn, without triaging which of them you can afford to include.

One honest caveat that applies to every long-context model, ours included: a big window is a capacity, not a guarantee of uniform attention across it. Structure still helps. Putting the question near the end, labeling your documents, and telling the model what to look for all measurably improve results at 150K tokens in a way they simply do not at 5K.

05Lite and Pro: shannon-3.1 and shannon-3.1-pro

The two tiers differ in how much of the reasoning loop they run, and that is the only difference that matters for choosing between them.

	shannon-3.1 (Lite)	shannon-3.1-pro (Pro)
Reasoning	Single pass	Full loop + knowledge harvest
Self-review	No	Yes
Context window	196,608	196,608
Streaming	Full speed, ungated	Full speed, ungated
Vision & documents	Yes	Yes
Image generation tool	Yes	Yes
Best for	Most work, high volume	Hard questions, first draft insufficient

Use Lite by default. One pass of a good reasoning model handles the large majority of real requests, and on 3.1 it is fast enough that the loop is no longer something you feel waiting through. Reach for Pro when the question is one where a first answer is usually wrong in an instructive way: architectural trade-offs, adversarial analysis, anything where you would want a competent colleague to sleep on it. Pro's self-review step is not decoration — it is the model finding its own errors before you have to.

06The 32% intelligence gain, and how to read it

Shannon Lab's own evaluation puts Shannon 3.1 at a 32% improvement in intelligence over Shannon 3.0. We want to be clear about what that is and is not.

It is our figure, from our internal evaluation suite, measured on tasks we consider representative of what people actually bring to these models. It is not a third-party benchmark, and we are not constructing a leaderboard table around a single internal aggregate or publishing a per-benchmark breakdown — a breakdown implies a level of external comparability an internal suite does not have.

What we will say is where the gain comes from, because that part is not mysterious. A larger context window means less material has to be discarded before the model reasons over it, and a great deal of apparent unintelligence in long sessions is really just amnesia. A serving stack we control means the model runs with the configuration we intended rather than a host default. And the reasoning loop — unchanged in design — now has room to actually run inside the window instead of being squeezed against a 32K ceiling that it shared with your input.

07Vision, documents, and image generation

Shannon 3.1 reads images and documents. Screenshots, diagrams, photographs, scanned pages, PDFs and text documents can go into the conversation and be reasoned over alongside everything else. Combined with the 196K window, this is what makes whole-document workflows practical: a long report and its charts in one turn, without deciding in advance which pages the model is allowed to see.

Image generation and editing are available in chat as a tool. Ask for an image and the model calls the tool inline, in the same conversation, with the context of everything discussed so far. Editing works the same way — hand it an image, describe the change. There is no separate mode to switch into and no separate interface to learn.

08Calling Shannon 3.1 from the API

Shannon 3.1 is available on all three API dialects, with streaming on each. Same models, three request shapes — pick whichever matches the SDK you already have.

Endpoint	Shape	Streaming
/v1/chat/completions	OpenAI-compatible	Yes
/v1/messages	Anthropic-compatible	Yes
/v1/responses	Responses	Yes

{
  "model": "shannon-3.1",
  "stream": true,
  "messages": [
    { "role": "user", "content": "Summarize this contract set and flag anything unusual." }
  ]
}

Swap "shannon-3.1" for "shannon-3.1-pro" to run the full reasoning loop. If you are already calling shannon-3, migration is the model id and nothing else: the request and response shapes are unchanged, and existing streaming clients keep working. The one behavioral difference to expect is the burstier stream described in section 02 — same tokens, same order, arriving sooner and in less even chunks.

Full parameter reference, authentication, error semantics and an interactive playground are in the API documentation. Other model cards and technical write-ups are in Shannon research.

09Still uncensored, and unchanged in that respect

Shannon 3.1 has no refusal layer and no content filtering on output. This is the same posture as the rest of the Shannon line and it did not change with the move to our own cluster — if anything, controlling the serving stack makes it easier to guarantee, because there is no intermediate host with its own policy sitting between the model and you.

Worth stating plainly because it is the obvious thing to wonder about a release that changed the whole output path: removing the pacing layer did not mean inserting a moderation layer in its place. Nothing inspects, rewrites, or gates the stream. What the model produces is what arrives. Shannon 3.1 is, as far as we are aware, the fastest uncensored AI model available with a context window this size — and the two properties are related, since both come from running our own infrastructure rather than renting compliance-shaped capacity from someone else.

Uncensored is not the same as unaccountable. Use is governed by our Responsible Use Policy, and the obligation that comes with a model that will engage with hard material is that you engage with it responsibly.

10Run the comparison yourself

Every claim here is checkable in about two minutes, and we would rather you checked than believed us.

1. Pick a prompt from work you actually do. Long is better — it exercises both the window and the streaming.
2. Run it on shannon-3. Note how long until the first token, and how long until the answer completes.
3. Run the identical prompt on shannon-3.1. Note the same two numbers.
4. Then read both answers, ignoring the clock, and decide which one you would have wanted.

The speed difference will be immediate and obvious. The quality difference is the one worth sitting with — it shows up most clearly on long inputs, where 3.0 was quietly working with less of your material than you thought it was.

11Frequently asked questions

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Performance and intelligence figures in this article are Shannon Lab's own measurements from internal evaluation, September 2026, and are stated as product figures rather than third-party benchmark results. Context window, model ids, and API availability are product specifications. Shannon AI is operated by Shannon Lab LLC, New Mexico, USA. Related reading: Shannon 3 · Shannon research index · API documentation.