Grok Voice 3.0 vs Realtime-Venus: The One With the Rate Card Cannot See, and the One That Can Has No Rate Card

There is a clean dividing line running through full-duplex voice in late 2026, and it is not quality. Grok Voice 3.0, announced by SpaceXAI on 14 October 2026, holds the top row of the Artificial Analysis Speech to Speech Index at 86.9 at $0.08 per minute of audio, and it perceives nothing but sound. Realtime-Venus, the two-checkpoint 9B system from Ant Group's Venus Team with Tsinghua University whose technical report went up on arXiv on 12 September 2026, streams camera frames through a SigLIP2 encoder and talks about what it sees — and it has no API, no price, no hosted endpoint and no independent evaluation of any kind. Everything you can buy hears. Everything that sees, you have to run yourself.

The perception gap is the entire comparison

These two do not differ by a few points on a shared benchmark. They differ by what they are physically able to take in, and the consequences run all the way down to what you can build.

• Input — Grok Voice 3.0 takes audio and text with a tools array; Realtime-Venus-Omni takes video or images, audio and text, with vision fused into the same network rather than bolted on.

• Turn-taking — Grok Voice 3.0 uses server-side voice activity detection configured per session; Realtime-Venus moves the decision inside the model, with no external detector at all.

• Output — Grok Voice 3.0 returns speech and transcripts; Realtime-Venus returns text and, optionally, a speech waveform decoded from discrete S3 tokens by a streaming flow-matching decoder rather than a separate text-to-speech stage.

• Reachability — Grok Voice 3.0 is live over SpaceXAI's realtime WebSocket, with code>grok-voice-latest/code> now resolving to it and its predecessor deprecated; Realtime-Venus is two BF16 checkpoints under Apache-2.0 at code>inclusionAI/Realtime-Venus/code> on Hugging Face, and the card states plainly that the model is not deployed by any inference provider.

• Measurability — Grok Voice 3.0 holds a composite index of 86.9 with five readable components and an arena Elo of 1070; Realtime-Venus has author-reported numbers from its own technical report and no leaderboard entry, because there is no endpoint for a third party to point a harness at.

• Price — $0.08 per minute of audio plus $0.004 per text input, against no price at all.

Read the last two lines together and the shape of the decision appears: the system with the audited numbers is the one that cannot see, and the one that can see has never been measured by anyone who did not build it.

What the report actually claims, and what it costs to check

Realtime-Venus's figures come from its own report and its model card, nobody outside the authors has reproduced them, and there is no arena placement to compare against. Read them as the authors' measurements.

• Video benchmarks, Realtime-Venus-Omni — best score on six of the eight benchmarks the report uses, including StreamingBench 70.2, OVO-Bench 64.7 and Daily-Omni 81.3.

• Audio benchmarks, Realtime-Venus-Audio — MMAU 78.0, MMAU-Pro 63.2, Llama Questions 83.8, Speech CMMLU 67.8 and a VoiceBench AlpacaEval score of 4.81 that the report describes as matching the best comparison figure.

• Full-Duplex-Bench v1.5 — response to 75% of user interruptions, with continuation rates of 97% under backchannels, 88% under other-directed speech and 86% under background speech, stated to exceed Gemini 3.1 Live and GPT-4o on all three continuation metrics.

• Context and format — 40,960 tokens on both checkpoints, BF16 weights, English and Chinese.

The Daily-Omni figure is the one worth pausing on, because it is where the report's own numbers do something useful. MiniCPM-o 4.5, the 9B omni-modal model OpenBMB open-sourced earlier in 2026, reports 80.2 on the same benchmark — and the Realtime-Venus card states the Omni checkpoint is adapted from it. Realtime-Venus-Omni reports 81.3. If both hold up, the improvement from a substantial post-training, runtime and delegation effort is roughly one point on a benchmark the base model already handled well. That is a realistic result for this kind of work and a much less dramatic story than "best on six of eight."

Against the scoreboard Grok Voice 3.0 sits on

Put the two side by side honestly and the comparison is asymmetric in a way that flatters neither.

On conversational dynamics, the only axis both systems report something for, Realtime-Venus's 97% continuation under backchannels is higher than the 97.0% Grok Voice 3.0 posts on Full Duplex Bench. Different harnesses, different versions, and one of the two has never been run by an outside party — so the honest reading is that Realtime-Venus claims parity or slightly better on the one metric where the numbers can even be mentioned in the same sentence, and that claim has one reader.

On everything else the two cannot be compared, because only one of them is on a board. Grok Voice 3.0's 86.9 composite decomposes into a reasoning figure near the ceiling, the 97.0% duplex score, 71.6% on τ-Voice, Elo 1070 on arena preference and 94.8% task success, each measured under a published harness that anyone can read and argue with. A composite is a weighting choice and the arena component is frozen at the point a model becomes eligible rather than tracking live Elo — but an outside team can check the arithmetic and disagree with the conclusion rather than being unable to form one.

That is the real difference in evidence quality, and it is worth stating without either side's adjectives. Both models carry vendor claims about their own production deployments. SpaceXAI reports Starlink call centres handling 20,000 customer calls a day in multiple languages with roughly 70% resolved without a human, plus millions of daily conversations across Tesla and SpaceX. Ant Group reports nothing about deployment at all, because there is no deployment — just a paper, a repository and a harness. Neither set is auditable from outside, and only one of them is even trying to be.

What running Realtime-Venus actually involves

The download is real. Two checkpoint directories with sharded safetensors, custom Transformers code loaded with code>trust_remote_code=True/code>, a requirements file, the token-to-waveform assets, a reference voice and an Apache-2.0 licence. Full-duplex streaming is entered with a single method switch, and the documented loop is one second of streaming prefill followed by streaming generation, repeated. Long-video memory is enabled with a memory-minutes parameter and is described as training-free, which is unusual for a capability that normally requires fine-tuning.

Two things about it matter more than the specs.

The first is the hardware floor. A 9B model in BF16 is roughly eighteen gigabytes of weights before activation memory, and audio-visual duplex inference adds the visual encoder and a continuous one-second streaming loop on top. That is a server-class deployment, not a laptop one — and it is a different proposition from a phone call that costs $0.08 a minute and starts working the afternoon you sign up.

The second is that the most interesting component is not in the weights. The dual-loop runtime — a one-second interaction loop plus a background scheduler the paper calls Realtime-Venus-Harness, which executes delegated tasks against an isolated snapshot of conversation state so a long-running job cannot be corrupted by the conversation moving on — lives in a separate GitHub repository. Causal task capture is the genuinely new idea in the report, and it is the part you assemble and trust yourself.

That last point is where the architecture of the two systems converges, and it is the practical thing to take from the pairing. Both are front ends. Grok Voice 3.0 calls tools from a tools array in its session update and delegates anything heavier to a backend that answers synchronously. Realtime-Venus emits an asynchronous delegation marker into its own hidden sequence and hands the work to a harness. In both designs the thinking, the retrieval and the business logic happen in an ordinary text model behind a conversational layer — and that layer is the one part of either stack you get to choose freely.

OrcaRouter carries neither Grok Voice 3.0 nor Realtime-Venus. The Grok Voice session belongs to SpaceXAI's own WebSocket, and Realtime-Venus is a download we do not serve; describing a router as sitting inside a duplex audio session would be describing something that does not exist. The delegated leg is different: 207 models from a dozen-plus upstreams behind one key at provider list price with zero markup, with automatic failover when a provider degrades and a routing DSL that composes several models into one call. For an architecture like Realtime-Venus's, where an unproven self-hosted component hands work to a text model you have not benchmarked either, failover is the difference between a bad afternoon for one provider and a dropped call.

Which one to pick, and when the answer changes

If you are shipping something this quarter, Grok Voice 3.0 is the only one of the two that is a decision rather than a project. It holds the top of the public board, it starts speaking in 0.81 seconds, it costs the same as the model it replaces, and it is callable today over an endpoint whose session parameters did not change. Pin your version first — the alias has already moved twice this quarter — and then ship.

Realtime-Venus is the right answer to a much narrower question: do you need a model that watches a room and talks about what is in it, on hardware you own, under a licence that lets you fine-tune it? That capability has no hosted equivalent in this pairing. Grok Voice 3.0 has no visual input at all, and neither do the other frontier voice models on the board. If that is your requirement, the download is the only door.

What would change this page is the same thing that would change most of this category. An inference provider carrying Realtime-Venus with a published rate would turn a research artefact into a product and make the comparison real; a second, independent reproduction of the 97% backchannel continuation figure would settle the one number the two systems can currently share. Until either lands, the accurate summary is blunt: one of these you can buy, and one of them you can read. The one you can read is the more interesting document, and the one you can buy is the one that answers the phone.