Model comparison
DeepSeek V4 Pro vs Kimi K3
DeepSeek against Moonshot AI, compared on context, price, and attributed benchmark results.
Recorded profile verification October 1, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below
DeepSeek
DeepSeek V4 Pro
Frontier · Open weights
Moonshot AI
Kimi K3
Frontier · Open weights
| Specification | DeepSeek V4 Pro | Kimi K3 |
|---|---|---|
| Provider | ||
| Provider | DeepSeek | Moonshot AI |
| Tier | ||
| Tier | Frontier | Frontier |
| Context window | ||
| Context window | 1M | Winner: 1.05M |
| Max output | ||
| Max output | 384K | Winner: 1.05M |
| Input / 1M tokens | ||
| Input / 1M tokens | Winner: $1.32 | $3 |
| Output / 1M tokens | ||
| Output / 1M tokens | Winner: $3.96 | $15 |
| Weights | ||
| Weights | Open | Open |
| Parameters | ||
| Parameters | 1.6T total / 49B active (MoE) | 2.8T total / 104B active (MoE) |
| Reasoning levels | ||
| Reasoning levels | low, high, max | low, high, max |
| Modalities | ||
| Modalities | text | text, image, video |
| License | ||
| License | MIT | Kimi K3 License (custom) |
| API model id | ||
| API model id | deepseek-v4-pro | kimi-k3 |
| Released | ||
| Released | April 24, 2026 | July 16, 2026 |
| Artificial Analysis Intelligence Index [max] · source date DeepSeek V4 Pro: 2026-10-01 | ||
| Artificial Analysis Intelligence Index [max] · source date DeepSeek V4 Pro: 2026-10-01 | 36 | Not verifiedUnverified |
| Artificial Analysis Intelligence Index [max] · source date Kimi K3: 2026-10-01 | ||
| Artificial Analysis Intelligence Index [max] · source date Kimi K3: 2026-10-01 | Not verifiedUnverified | 44 |
| SWE-bench Verified [max] · source date DeepSeek V4 Pro: 2026-04-24 | ||
| SWE-bench Verified [max] · source date DeepSeek V4 Pro: 2026-04-24 | 80.6 | Not verifiedUnverified |
| SWE-bench Verified · source date Kimi K3: 2026-10-01 | ||
| SWE-bench Verified · source date Kimi K3: 2026-10-01 | Not verifiedUnverified | 93.4 |
| GPQA Diamond [max] · source date DeepSeek V4 Pro: 2026-04-24 | ||
| GPQA Diamond [max] · source date DeepSeek V4 Pro: 2026-04-24 | 90.1 | Not verifiedUnverified |
| GPQA Diamond [max] · source date Kimi K3: 2026-10-01 | ||
| GPQA Diamond [max] · source date Kimi K3: 2026-10-01 | Not verifiedUnverified | 93.5 |
| Humanity's Last Exam [max] · source date DeepSeek V4 Pro: 2026-04-24 | ||
| Humanity's Last Exam [max] · source date DeepSeek V4 Pro: 2026-04-24 | 48.2 | Not verifiedUnverified |
| Humanity's Last Exam · source date DeepSeek V4 Pro: 2026-08-13 | ||
| Humanity's Last Exam · source date DeepSeek V4 Pro: 2026-08-13 | 42.7 | Not verifiedUnverified |
| Humanity's Last Exam · source date DeepSeek V4 Pro: 2026-08-13 | ||
| Humanity's Last Exam · source date DeepSeek V4 Pro: 2026-08-13 | 60 | Not verifiedUnverified |
| Humanity's Last Exam [max] · source date Kimi K3: 2026-10-01 | ||
| Humanity's Last Exam [max] · source date Kimi K3: 2026-10-01 | Not verifiedUnverified | 56 |
| MMLU-Pro [max] · source date DeepSeek V4 Pro: 2026-04-24 | ||
| MMLU-Pro [max] · source date DeepSeek V4 Pro: 2026-04-24 | 87.5 | Not verifiedUnverified |
| Terminal-Bench 2.1 · source date DeepSeek V4 Pro: 2026-08-13 | ||
| Terminal-Bench 2.1 · source date DeepSeek V4 Pro: 2026-08-13 | 87.9 | Not verifiedUnverified |
| Terminal-Bench 2.1 [max] · source date Kimi K3: 2026-10-01 | ||
| Terminal-Bench 2.1 [max] · source date Kimi K3: 2026-10-01 | Not verifiedUnverified | 88.3 |
| DeepSWE 1.1 · source date DeepSeek V4 Pro: 2026-08-13 | ||
| DeepSWE 1.1 · source date DeepSeek V4 Pro: 2026-08-13 | 62.7 | Not 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 date2026-10-01Vals.ai exact Kimi K3 93.40% key-takeaway result; independent bash-only harness, 500-task Verified subset; experiment date unverified
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.
- Source date2026-10-01Kimi K3 official model-card table (vendor-reported) (HLE-Full with general tools; temperature 1.0; 43.5% separately without tools; exact run date unverified)
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 date2026-10-01Kimi K3 official model-card table (vendor-reported) (Single-step, temperature 1.0, top-p 0.95; exact run date unverified)
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 date2026-10-01Kimi K3 official model-card table (vendor-reported) (Kimi Code harness; temperature 1.0, top-p 1.0; exact run date unverified)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.
- Source date2026-10-01Artificial Analysis (Kimi K3 (max))
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
- Source date2026-10-01Artificial Analysis (DeepSeek V4 Pro 0813 (max))
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
- Source date2026-08-13DeepSeek August 13 GA changelog (vendor) (0813 GA refresh; vendor report, exact run configuration unverified)
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
- Source date2026-08-13DeepSeek August 13 GA changelog (vendor) (0813 GA refresh; vendor report, exact run configuration unverified)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.
- Source date2026-08-13DeepSeek August 13 GA changelog (vendor) (0813 GA refresh; with tools; vendor report, exact run configuration unverified)
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 date2026-08-13DeepSeek August 13 GA changelog (vendor) (0813 GA refresh; without tools; vendor report, exact run configuration unverified)
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 date2026-04-24DeepSeek V4 Pro HF model card (vendor, Think Max, exact match) (Original April 24 checkpoint; historical vendor evaluation, not the served 0813 refresh)
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 date2026-04-24DeepSeek V4 Pro HF model card (vendor, Think Max, with tools; 37.7 no tools) (Original April 24 checkpoint; historical vendor evaluation, not the served 0813 refresh)
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 date2026-04-24DeepSeek V4 Pro HF model card (vendor, Think Max, pass@1) (Original April 24 checkpoint; historical vendor evaluation, not the served 0813 refresh)
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 date2026-04-24DeepSeek original V4 Pro HF card (vendor, Think Max; verifier/harness comparability unverified) (Original April 24 checkpoint; historical vendor evaluation, not the served 0813 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 Pro: Official peak rates $1.32/$3.96 per MTok; off-peak $0.66/$1.98 (cache-miss). Cache hit $0.044 peak / $0.022 off-peak. Peak hours are weekdays 01:00–04:00 and 06:00–10:00 UTC, excluding Chinese public holidays; weekends, holidays and all other hours are off-peak. Open weights (MIT). 1.6T/49B active MoE.
Kimi K3: $3/$15 per million input/output tokens; cached input $0.30. Cache writes add $3/MTok for 5-minute TTL or $6/MTok for 1-hour TTL; hits refresh TTL without another write charge. API access requires a successful top-up of at least $1. max_completion_tokens defaults to 131,072 and can be set up to 1,048,576. Kimi K3 uses a custom license; self-hosting has compute costs.
DeepSeek V4 Pro
DeepSeek V4 Pro is DeepSeek's large open-weight (MIT) MoE with a 1M-token context — DeepSeek says the cheaper V4.1-Flash now beats it.
Best for
- Cost-sensitive hosted agents
- Open-weight deployments
- High-volume coding
Watch out
The current tariff identifies DeepSeek-V4-Pro-0813. DeepSeek's current changelog reverses the earlier September 14 re-route plan and says it will continue serving V4 Pro at unchanged rates until further notice. Original April benchmark/checkpoint results are not the current 0813 evaluation. Self-hosting memory depends on quantization, offloading and hardware.
Kimi K3
Kimi K3 is Moonshot AI's open-weight multimodal 2.8T/104B MoE model with a 1,048,576-token context.
Best for
- Open-weight frontier work
- Long-context
- Multimodal
Watch out
Thinking is always on; preserve complete assistant reasoning/tool history. Vision input requires base64 or ms:// rather than public image URLs, and temperature/top_p/n/penalties are fixed. Current docs discourage production web-search use. The custom licence requires an agreement for Model-as-a-Service businesses above $20M aggregate licensee/affiliate revenue over consecutive 12 months; commercial products above 100M monthly active users or $20M monthly revenue require Kimi K3 UI attribution. Internal use and official/certified serving routes have separate exemptions. July 16 is Kimi Code's release date, distinct from weights publication.
When the cheaper one wins
DeepSeek V4 Pro is cheaper on output at $3.96 per million tokens against $15 for Kimi K3 — about 3.8×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. On DeepSWE 1.1, DeepSeek V4 Pro is 62.8% Pass@1 at $0.24/task versus Kimi K3 at 68.5% / $4.65/task. This compares standard short-context token rates, not total cost per accepted task. Include input, cache, context tiers, schedules, retries and review in your pilot.
Run the model pickerEvidence confidence: Medium
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 5/5 core specs verified on both sides — All core specifications verified for both models.
- 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.
- DeepSeek V4 Pro: Whole-profile primary source check October 1 (accessed 2026-10-01)
- DeepSeek V4 Pro: Whole-profile primary source check October 1 (accessed 2026-10-01)
- DeepSeek V4 Pro: DeepSeek current API version, holiday tariff and 0813 GA benchmark publication (accessed 2026-10-01)
- DeepSeek V4 Pro: DeepSeek current tariff and served endpoint versions (accessed 2026-10-01)
- DeepSeek V4 Pro: DeepSeek — V4 news (accessed 2026-08-29)
- DeepSeek V4 Pro: DeepSeek API pricing (accessed 2026-08-29)
- DeepSeek V4 Pro: DeepSeek — V4.1-Flash release (V4-Pro phase-out) (accessed 2026-09-26)
- DeepSeek V4 Pro: DeepSeek API changelog (accessed 2026-09-26)
- Kimi K3: Whole-profile primary source check October 1 (accessed 2026-10-01)
- Kimi K3: Whole-profile primary source check October 1 (accessed 2026-10-01)
- Kimi K3: Kimi Code dated release history (K3 July 16; distinct from weights publication) (accessed 2026-10-01)
- Kimi K3: Kimi K3 API pricing — native cache-write TTL charges (accessed 2026-10-01)
- Kimi K3: Exact Kimi K3 custom license (accessed 2026-10-01)
- Kimi K3: HuggingFace — Kimi K3 (accessed 2026-08-29)
- Kimi K3: Moonshot — Kimi K3 guide (accessed 2026-08-29)
- Kimi K3: Kimi K3 quickstart (max_completion_tokens up to 1,048,576) (accessed 2026-09-26)
Related comparisons
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- Claude Fable 5.1 vs Kimi K3
- Claude Fable 5 vs DeepSeek V4 Pro
- Claude Fable 5 vs Kimi K3
- Claude Mythos 5.1 vs DeepSeek V4 Pro
- Claude Mythos 5.1 vs Kimi K3
Diving deeper on one model? DeepSeek V4 Pro · Kimi K3
Common questions
DeepSeek V4 Pro vs Kimi K3
Answered from the verified figures on this page rather than general guidance.
Is DeepSeek V4 Pro or Kimi K3 cheaper for input?
Is DeepSeek V4 Pro or Kimi K3 cheaper for output?
Which has the larger context window, DeepSeek V4 Pro or Kimi K3?
Do DeepSeek V4 Pro and Kimi K3 support the same reasoning levels?
Should I use DeepSeek V4 Pro or Kimi K3?
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Input price: DeepSeek V4 Pro costs $1.32 per 1M tokens versus $3 for Kimi K3 — a 2.3x difference at the headline tier.
- Context: Kimi K3 takes 1.05M against 1M for DeepSeek V4 Pro — only decisive if your prompts approach the smaller window.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.