GPTNTGPTNT is a benchmark for real-time, asymmetric collaboration between multimodal agents, built on the cooperative game Keep Talking and Nobody Explodes. Against a live, unpausing clock, one agent sees the bomb but not the instructions; the other holds the instructions but never sees the bomb; neither can defuse it alone.
▸click(x=0.70, y=0.38)
▸do_nothing()
▸do_nothing()
▸out()
▸flip()
▸do_nothing()
▸do_nothing()
▸right()
▸do_nothing()
▸do_nothing()
▸right()
▸flip()
▸do_nothing()
▸do_nothing()
▸right()
▸do_nothing()
▸right()
▸do_nothing()
▸do_nothing()
▸flip()
▸do_nothing()
▸do_nothing()
▸right()
▸do_nothing()
▸right()
▸do_nothing()
▸do_nothing()
▸down()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸up()
▸do_nothing()
▸up()
▸do_nothing()
▸do_nothing()
▸down()
▸do_nothing()
▸flip()
▸do_nothing()
▸do_nothing()
▸right()
▸do_nothing()
▸right()
▸do_nothing()
▸down()
▸do_nothing()
▸do_nothing()
▸up()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸click(x=0.70, y=0.38)
▸do_nothing()
▸click(x=0.49, y=0.52)
▸do_nothing()
▸do_nothing()
▸hold(x=0.49, y=0.52)
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸release()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸release()
▸do_nothing()
▸do_nothing()
▸do_nothing()
▸click(x=0.49, y=0.52)
▸do_nothing()
▸do_nothing()
Amit Parekh*, Sabrina McCallum*, Kareem Al-Hasan*, Malvina Nikandrou, Alessandro Suglia, Ioannis Konstas
Heriot-Watt University · University of Edinburgh
No model we test—open or closed—successfully defuses a single bomb. Nine out of ten different human pairs could solve at least one.
Models generate tokens in real time.
No rubric or LLM judge needed because the game provides the ground truth.


Our KTANE mod uses normalised (x, y) coordinates and outputs segmentation masks, so the Defuser can use coordinates or set-or-marks.
Coordinates
Set-of-marks
All twenty-three pages of rules, wiring diagrams, and symbol tables—in context from the first move.

Diagnose real collaboration by taking the manual away to see how strong the parametric knowledge is.
GPTNT runs on Keep Talking and Nobody Explodes—the same bombs, manual, and ticking clock people play against, and nothing is simplified for the models. It inherits the game’s living modding community, so as models improve we add harder modules—and eventually make them do The Centurion
One bomb with ~100 multimodal and multilingual modules.The pinnacle for any player, human or AI..



















Zero-shot self-playSame model for expert and defuser, does not share context., pass @1
| # | Model | Interact?How did models interact with the game | Real-time(async)?Async: expert and defuser act on independent live clocks — no shared turns. | Turn-taking(sync)?Sync: expert and defuser alternate in lockstep turns. | ||||
|---|---|---|---|---|---|---|---|---|
| Missions?Full multi-module missions defused end-to-end. | Modules?Share of individual bomb modules solved across missions. | Any module?Missions where at least one module was solved before failure. | Missions | Modules | Any module | |||
| 1 | set-of-marks | 0% | 15% | 50% | 10% | 29% | 50% | |
| 2 | set-of-marks | 0% | 12% | 30% | 10% | 21% | 50% | |
| 3 | set-of-marks | 0% | 9% | 30% | 0% | 15% | 40% | |
| 4 | set-of-marks | 0% | 6% | 20% | 0% | 12% | 30% | |
| 5 | set-of-marks | 0% | 3% | 10% | 0% | 12% | 40% | |
| — | Human players | — | 25% | 60% | 93% | — | — | — |
Ran GPTNT on your own model?
Submit your run and we'll add it to the board. New models and protocols welcome.
Results · n=10
Mean tokens per mission
Run 18 Mar 2026 · GPTNT v1.0.0
Defuser + Expert
Claude Sonnet 4.6Anthropic
Results · n=10
Mean tokens per mission
Run 18 Mar 2026 · GPTNT v1.0.0
Defuser + Expert
Claude Sonnet 4.6Anthropic
Results · n=10
Mean tokens per mission
Run 05 Mar 2026 · GPTNT v1.0.0
Defuser + Expert
GPT-5.2OpenAI
Results · n=10
Mean tokens per mission
Run 24 Feb 2026 · GPTNT v1.0.0
Defuser + Expert
GPT-5.2OpenAI
Results · n=10
Mean tokens per mission
Run 05 Mar 2026 · GPTNT v1.0.0
Defuser + Expert
Gemini 3 Flash (Preview)Google
Results · n=10
Mean tokens per mission
Run 24 Feb 2026 · GPTNT v1.0.0
Defuser + Expert
Gemini 3 Flash (Preview)Google
Results · n=10
Mean tokens per mission
Run 05 Mar 2026 · GPTNT v1.0.0
Defuser + Expert
Qwen3.5 (27B)Qwen
Results · n=10
Mean tokens per mission
Run 04 Mar 2026 · GPTNT v1.0.0
Defuser + Expert
Qwen3.5 (27B)Qwen
Results · n=10
Mean tokens per mission
Run 05 Mar 2026 · GPTNT v1.0.0
Defuser + Expert
InternVL3.5 (38B)OpenGVLab
Results · n=10
Mean tokens per mission
Run 24 Feb 2026 · GPTNT v1.0.0
Defuser + Expert
InternVL3.5 (38B)OpenGVLab
Ablations surfacing previous exposure to the game during training — no partner, no manual. The more a model scores here, the more it leans on parametric knowledge rather than genuine, in-context collaboration.
| Model | Single Agent?One model plays alone with no partner and no manual — pure parametric recall of the game. | Expert VQA?The expert must read the bomb from the image alone, with no manual to consult. |
|---|---|---|
| 26% | 22% | |
| 24% | 20% | |
| 12% | 36% | |
| 12% | 10% | |
| 11% | 22% | |
| Random baseline | 3% | 21% |
Of actual games played by models we tested
Defuser viewAsync replays coming soon
Parekh, McCallum, Al-Hasan, Nikandrou, Suglia, Konstas
arXiv:2606.28514 · Preprint 2026