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The official API serves V4.1 Flash as deepseek-flash and accepts text and images. Sources below distinguish vendor benchmarks, independent evaluations, and personal reports. Availability in Tabbit depends on your account's live model picker.
Use in Tabbit DeepSeek V4.1 Flash
DeepSeek V4.1 Flash · Model overview
A compact view of reviewed task guides, public evaluations, and evidence boundaries. Client access still depends on your current account.
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Extract structured data, build a visual prototype, or start a coding task.
Use deepseek-flash as the model name for the first call; the old aliases are still accepted, but requests are served by DeepSeek-V4.1-Flash and billed at Flash pricing. The official example uses an OpenAI-compatible format, thinking enabl
The current API uses deepseek-flash to access DeepSeek-V4.1-Flash; thinking mode is enabled by default with a default effort of high, request tiers map to low, high, or max according to the official mapping, and multi-turn requests with t
An English systemprompt that includes an example can make the DeepSeek API extract question-and-answer text into JSON; the call must also set responseformat to {'type': 'jsonobject'}. The official documentation also notes that the prompt
Tool calling is a two-stage, client-driven process: the model first returns toolcalls, the client executes the function and sends back the role="tool" result, and the model then generates the final answer. When strict beta is enabled, the
Public results use different versions, tiers, and harnesses; unknown values stay unknown.
The official model card shows that DeepSeek-V4.1-Flash is a multimodal MoE with a 552B backbone and 8B (prefill) / 16B (decode) activated per token; with the specified maximum reasoning effort and Agent harness, it scores 90.6 on Terminal-Bench 2.1 and 74.2 on DeepSWE v1.1, both above DeepSeek-V4-Flash in the table. Those numbers demonstrate the combined performance of the “model + harness + parameters” configuration and cannot be generalized as uniform capability across all clients or ordinary conversational tasks.
Arena.ai reports a **+4.87%** net improvement for DeepSeek-V4.1-Flash (Max) relative to the Arena baseline. The body of the post states a **$0.07** median cost per task, while the detailed table states **$0.06**. The cost should therefore be recorded as an inconsistency in the original post, rather than combined into one unconditionally certain figure.
At the `Reasoning, Max Effort` setting, Artificial Analysis gives DeepSeek V4.1 Flash an Artificial Analysis Intelligence Index score of about 40. It is attractive for agent tasks, long-context work, and cost, but its outputs are extremely verbose, and its AA-Omniscience Non-Hallucination Rate is only 4%, so “cheap” cannot be equated directly with reliable.
DeepSeek
Explore reusable DeepSeek V4.1 Flash prompts, vision and tool-calling configurations, agent benchmarks, task costs, and firsthand user reports.