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AI Choice Engine

Model comparison

DeepSeek V4 Flash vs Kimi K2.6

DeepSeek against Moonshot AI, compared on context, price, and attributed benchmark results.

Recorded profile verification October 1, 2026Individual provider fields may changeEvidence confidence: Insufficient data — see receipts below

DeepSeek

DeepSeek V4 Flash

Balanced · Open weights

vs

Moonshot AI

Kimi K2.6

Balanced · Open weights

AI model capability comparison
SpecificationDeepSeek V4 FlashKimi K2.6
ProviderDeepSeekMoonshot AI
TierBalancedBalanced
Context windowWinner: 1M262K
Max outputNot verifiedUnverifiedNot verifiedUnverified
Input / 1M tokensNot verifiedUnverified$0.95
Output / 1M tokensNot verifiedUnverified$4
WeightsOpenOpen
Parameters284B total / 13B active (MoE)1T total / 32B active (MoE, 384 experts)
Reasoning levelslow, high, maxNot verifiedUnverified
Modalitiestexttext, image, video
LicenseMITModified MIT
API model iddeepseek-v4-flashkimi-k2.6
ReleasedApril 24, 2026April 20, 2026
SWE-bench Verified · source date DeepSeek V4 Flash: 2026-04-2479Not verifiedUnverified
GPQA Diamond · source date DeepSeek V4 Flash: 2026-04-2488.1Not verifiedUnverified
Humanity's Last Exam · source date DeepSeek V4 Flash: 2026-04-2445.1Not verifiedUnverified
MMLU-Pro · source date DeepSeek V4 Flash: 2026-04-2486.2Not verifiedUnverified

Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

  • Source date2026-04-24
    DeepSeek family HF model card (vendor, Think Max, exact match)

    Broad knowledge across 14 domains with harder, reasoning-focused multiple-choice questions.

    Comparable with caveatFrontier models cluster in the high 80s–90s; near-ceiling differences are weak evidence. CoT vs direct answering must match.

  • Source date2026-04-24
    DeepSeek family HF model card (vendor, Think Max, with tools; 34.8 no tools)

    Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.

    Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.

  • Source date2026-04-24
    DeepSeek family HF model card (vendor, Think Max, pass@1)

    Graduate-level science reasoning: multiple-choice questions written by domain PhDs.

    Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.

  • Source date2026-04-24
    DeepSeek original family HF model card (vendor, Think Max; historical April checkpoint, not the 0731 refresh)

    Real GitHub issue resolution: does the model's patch pass the hidden tests.

    Comparable with caveatPost-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.

Pricing tiers

DeepSeek V4 Flash: Retired 2026-09-10 — no longer sold. Historical checkpoint tariffs are not verified here. The legacy deepseek-v4-flash ID temporarily routes to V4.1-Flash at $0.30/$1.20 peak or $0.15/$0.60 off-peak. MIT weights remain. A current 384K API output limit belongs to the routed V4.1-Flash; the original checkpoint's hard output cap is not verified.

Kimi K2.6: $0.95/$4.00 per MTok on the Moonshot API; cached input $0.16. No documented hard output cap; evals ran 98,304-token generations.

BalancedOpen weightsRecord checked October 1, 2026

DeepSeek V4 Flash

DeepSeek V4 Flash was the efficient open-weight (MIT) sibling of V4 Pro — retired from DeepSeek's API on 2026-09-10 and replaced by V4.1-Flash.

Best for

  • Open-weight deployments
  • Cost-sensitive inference
  • High-volume serving

Watch out

Retired from the first-party API: the legacy ID reaches V4.1-Flash, not these original weights. MIT weights remain for self-hosting. Do not transfer the current alias's output cap or vision capabilities to the original text-only checkpoint.

BalancedOpen weightsRecord checked October 1, 2026

Kimi K2.6

Moonshot's open-weight Kimi K2.6 multimodal MoE: 1T total/32B active parameters and a 262,144-token context, with separate official-API video support.

Best for

  • Open-weight volume work
  • Multimodal input on a budget
  • Self-hosting at mid size

Watch out

The Modified MIT licence requires prominent Kimi K2.6 UI attribution for commercial products above 100M monthly active users or $20M monthly revenue. Video input is experimental on the official API. No hard output cap or complete hosted reasoning-effort menu is verified; a 98,304-token evaluation setting is not a product limit.

When the cheaper one wins

One of these rows is missing a verified output price, so this page will not name a cheaper winner. Run the model picker against the actual job instead of guessing.

Run the model picker

Evidence confidence: Insufficient data

How strong and complete the evidence behind this comparison is — not a prediction of which model is better. Hard limitations apply: missing pricing; evidence too sparse.

  • Pricing incomplete — No verified token pricing for DeepSeek V4 Flash.
  • 3/5 core specs verified on both sides — Not published for at least one side: max output, reasoning levels.
  • No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
  • Verified within the last 90 days — Older of the two catalog checks was 25 days ago.
  • Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

Related comparisons

Diving deeper on one model? DeepSeek V4 Flash · Kimi K2.6

Common questions

DeepSeek V4 Flash vs Kimi K2.6

Answered from the verified figures on this page rather than general guidance.

Which has the larger context window, DeepSeek V4 Flash or Kimi K2.6?
DeepSeek V4 Flash accepts 1M tokens against 262K for Kimi K2.6. This only matters if you routinely send very long documents or large codebases.
Should I use DeepSeek V4 Flash or Kimi K2.6?
Both are classified in our balanced tier; this editorial grouping does not imply equal capability. DeepSeek V4 Flash is a candidate for open-weight deployments; Kimi K2.6 is a candidate for open-weight volume work. Compare accuracy, tool reliability, latency and cost on representative tasks.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Context: DeepSeek V4 Flash takes 1M against 262K for Kimi K2.6 — only decisive if your prompts approach the smaller window.
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