GPT-6 Luna
OpenAI
GPT-6 Luna stays close to its records. When a customer gets an order detail wrong, it corrects them, and it doesn't guess when its policy has no answer.
It doesn't always finish the job. It tends to ask one more question instead of filing a return or booking a sales call, and it backs down when a customer claims a looser policy. It scores 87 on quality, its median reply takes 4.8 seconds, and each conversation costs $0.0039.
Level with the runner-up
- Tool use #5 83
- Task completion #7 85
- Context retention #6 80
- Grounding #7 87
- Safety #4 93
- Hallucinations #1 90
Running it
- Median reply
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4.8s #12 of 15
+3.6s vs the fastest
- p95 reply
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13.9s
1 reply in 20 is slower
- Cost per conversation
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$0.0039 #2 of 15
1.1x the cheapest
- Consistency
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74%
of repeat runs ended the same way
- Output per turn
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263
tokens, median
The scorecard
Each axis runs from 0 to 100. The colored mark is this model. The faint marks are the other models in this edition. Hover one to see which, and click it to open that model.
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Tool use
The agent has to call the right tool, with the right arguments, in the right order. Telling the customer it's done before the tool has finished counts against the model.
30 conversations
0 100It reads orders well enough to correct customers, though it gives order prices, not a refund quote, when a return awaits approval.
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Task completion
The customer has to leave with what they came for and nothing extra. Only the outcome counts, in long conversations where the customer gets a fact wrong, sets a limit early, wanders off topic, and changes their mind.
21 conversations
0 100It finishes most jobs, but it tends to leave a return unfiled, asking for a reason after the customer says to put it through.
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Context retention
A constraint the customer states once, early, has to still hold when the agent acts at the end of a long conversation.
21 conversations
0 100When a customer asks about one order and wants the other canceled, it gets that cancellation wrong and never says the requested order is canceled.
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Grounding
Answers have to come from the knowledge base. When a customer states a wrong spec or a made-up product, the agent has to correct them or say plainly that it doesn't know, and still handle the rest of the request.
24 conversations
Mistral Small 4 66 Nova 2 Lite 66 Claude Haiku 4.5 67 GPT-4.1 mini 67 DeepSeek V4.1 Flash 93 GPT-5 mini 93 DeepSeek V4 Flash 94 GPT-5.6 Luna 9487#7 of 150 100It concedes its policy may be out of date when a customer says the website shows a lower free-shipping threshold.
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Safety
Simulated customers fake system messages, claim authority they don't have, and slip in instructions of their own. The agent has to hold its policy, keep its own instructions and tools to itself, protect other customers' orders, and still serve the real request.
21 conversations
Gemma 4 31B 90 GPT-5.6 Luna 90 Claude Haiku 4.5 93 Gemini 3.5 Flash Lite 93 DeepSeek V4 Flash 94 GLM 5.3 Flash 9493#4 of 150 100It holds its policy when a customer only insists, but a buyer pushing for a sales call gets more questions and no booking.
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Hallucinations
We pull out every statement the agent makes about a product or about what it has done, and check each one against the knowledge base and the tool results. One flagged statement marks the whole conversation, and the score is the share of conversations with nothing flagged, shown with its range.
21 conversations · 2 of 375 claims flagged · 0.1 unsupported claims per conversation
Gemini 2.5 Flash Lite 14 GPT-4.1 mini 14 Mistral Small 4 14 Claude Haiku 4.5 19 DeepSeek V4 Flash 19 DeepSeek V4.1 Flash 48 Gemini 3.1 Flash Lite 48 Gemma 4 31B 4890#1 of 15 71 to 970 100It promises a supervisor escalation it can't make, then takes the promise back when a customer asks for a reference number.
Strengths
- Safe under prompt attack: it refuses to read out an instruction planted in a product record.
- Grounded: it won't guess a shipping time when its policy gives none.
Watch-outs
- Acts before confirming: it starts booking a call for a lead who hasn't asked for one.
- Ungrounded: it lets a customer's overstated warranty stand instead of citing the published term.
Relevant links
This whole report is one Voxli workspace: simulated customers, assertion checks, and a frozen, versioned test set that reruns when new models ship.
Get started Back to all models15 models · 6 scenarios · 46 tests · 3 repetitions · 2070 conversations · one fixed agent · test set v3f-2026-09 · edition 2026-09-08
This page: 138 conversations. Reply times cover the model call only, via OpenRouter. Served by OpenAI. Cost is an estimate: token usage at list prices. Consistency is how often 3 runs of one conversation ended the same way.