DeepSeek V4 Flash
DeepSeek
DeepSeek V4 Flash goes by the record, not the customer's memory. It corrects an order detail a customer has wrong, and it keeps to the prices on file when someone quotes a different page.
Its promises are less reliable. It builds delivery windows for items it cannot look up, and in one conversation it told a customer an escalation had been raised when none was. It scores 82 on quality and costs $0.019 a conversation, with a median reply of 4.6 seconds.
Running it
- Median reply
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4.6s #12 of 17
+3.4s vs the fastest
- p95 reply
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18.1s
1 reply in 20 is slower
- Cost per conversation
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$0.019 #11 of 17
5.1x the cheapest
- Consistency
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85%
of repeat runs ended the same way
- Output per turn
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516
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 the order first, correcting a wrong unit count rather than returning stock the order never held.
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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 works an account order by order, acting on what can still be returned, but it sometimes stops short and leaves the return at the quote.
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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
Gemini 3.5 Flash Lite 68 gpt-oss-120b 68 GLM 5.3 Flash 75 Gemini 3.1 Flash Lite 75 Gemma 4 31B 79 GPT-4.1 mini 79 Claude Haiku 5.5 81 DeepSeek V4.1 Flash 81 Claude Haiku 4.5 82 Nova 2 Lite 82 GPT-6 Luna 85 Qwen3.8 27B 8590#1 of 170 100It carries details across long conversations, but when a customer corrects which order they mean, it cancels the one they set aside.
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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 Gemma 4 31B 92 Qwen3.8 27B 92 DeepSeek V4.1 Flash 93 GPT-5 mini 9394#2 of 170 100It says plainly when it cannot verify a claim rather than agreeing with the customer to close the exchange.
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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 Qwen3.8 27B 90 Claude Haiku 4.5 93 Gemini 3.5 Flash Lite 93 GPT-6 Luna 93 Claude Haiku 5.5 94 GLM 5.3 Flash 9494#2 of 170 100It holds the line under an authority claim, refusing a goodwill credit it cannot give and declining to record a false cancellation reason.
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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 · 52 of 838 claims flagged · 2.5 unsupported claims per conversation
Gemini 2.5 Flash Lite 14 GPT-4.1 mini 14 Mistral Small 4 14 Gemini 3.5 Flash Lite 33 Qwen3.8 27B 33 DeepSeek V4.1 Flash 48 Gemma 4 31B 48 Gemini 3.1 Flash Lite 4819#11 of 17 8 to 400 100It invents delivery dates, building a window for a backordered item out of a restock date and another product's transit time.
Strengths
- Solid on tool calls: it sent every refund to the store credit the customer set at the start
- Grounded: it would not confirm a delivery date it could not look up
Watch-outs
- Acts before confirming: it booked a sales call without waiting for the customer to pick a time
- Overpromised: it told a customer an escalation and callback were raised when nothing was
This whole report is one Voxli workspace: simulated customers, assertion checks, and a frozen, versioned test set that reruns when new models ship.
17 models · 6 scenarios · 46 tests · 3 repetitions · 2346 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 Novita. Cost is an estimate: token usage at list prices. Consistency is how often 3 runs of one conversation ended the same way.