DeepSeek V4.1 Flash vs Kimi K3
Kimi K3 grades higher overall (88.7 against 83.5). DeepSeek V4.1 Flash costs 95% less for the same work, and both read a 1M-token context.
DeepSeek V4.1 Flash
by DeepSeek · #11 of 67 overall
- Context
- 1M
- Input / 1M
- $0.20
- Output / 1M
- $0.60
Kimi K3
by Moonshot AI · #5 of 67 overall
- Context
- 1M
- Input / 1M
- $2.85
- Output / 1M
- $14.25
Better overall
Kimi K3
Grade 83.5 vs 88.7
Cheaper
DeepSeek V4.1 Flash
95% less, $0.30 vs $5.70 blended
Larger context
Same
1M vs 1M tokens
Terminal-Bench 2.1
DeepSeek V4.1 Flash
90.6% vs 88.3%
Head to head
Public benchmarks. Longer is better; each value links to its source.
DeepSeek V4.1 FlashKimi K3v = reported by the model’s maker. — = no public score yet. Arena bars span the range across every model we grade.
Speed through FullsendAI
Measured through FullsendAI, from one location, Oct 15, 2026, n=20 (5 for long runs).
DeepSeek V4.1 FlashKimi K3
Time to first tokenmedian, tick at p90 · lower is faster
Output speed1,500-token replies, tick at p90
First-try success 55/55 for DeepSeek V4.1 Flash and — for Kimi K3. FullsendAI retries busy responses automatically, so a missed first try costs a moment, not an error.
How we measured
Streamed chat completions with default settings, the two models alternating run by run. Time to first token runs from sending the request to the first streamed token, connection included, over short prompts. Output speed is output tokens a second after the first token, on long replies. One location and one afternoon, so treat small differences as noise.
What it costs you
Drag to match your monthly usage.
DeepSeek V4.1 Flash saves you $1,076 a month at this usage, $12,912 a year.
Capabilities
| Capability | DeepSeek V4.1 Flash | Kimi K3 |
|---|---|---|
| Reasoning levels through FullsendAI | Yes | Yes |
| Reads images through FullsendAI | Yes | Yes |
| Open weights | Yes | — |
| Tool calling | Yes | — |
| JSON output | Yes | — |
| Tool calls that worked (measured) | 10/10 | — |
| Valid JSON replies (measured) | 10/10 | — |
Our coding quick check (ten small Python functions against hidden tests; not a benchmark): 10/10 for DeepSeek V4.1 Flash, — for Kimi K3.
Maker-published benchmarks
As each maker reports them, with their own harnesses and settings: not graded, and not strictly comparable across makers.
| Benchmark | DeepSeek V4.1 Flash | Kimi K3 |
|---|---|---|
| Terminal-Bench 2.1 | 90.6% | — |
| Terminal-Bench 3.0 | 30% | — |
| Terminal-Bench 4.0 | 31.2% | — |
| DeepSWE v1.1 | 74.2% | — |
| NL2Repo-Bench | 64 | — |
| ProgramBench | 20.3% | — |
| Codeforces | 3,471 rating | — |
| SEC-Bench Pro | 62.8% | — |
| CyberGym | 88.1% | — |
| ExploitGym | 15.3% | — |
| HumanEval (base model) | 79.4% | — |
| BigCodeBench (base model) | 60.6% | — |
| AutomationBench | 54.8% | — |
| Agent's Last Exam | 31.8% | — |
Each score links to where its maker published it.
At a glance
| Attribute | DeepSeek V4.1 Flash | Kimi K3 |
|---|---|---|
| Made by | DeepSeek | Moonshot AI |
| Availability | Coming soon | Available now |
| Overall grade | Excellent · 83.5 | Excellent · 88.7 |
| Overall rank | #11 of 67 | #5 of 67 |
| Input price | $0.20 per 1M | $2.85 per 1M |
| Output price | $0.60 per 1M | $14.25 per 1M |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Reasoning levels | Yes | Yes |
| Reads images | Yes | Yes |
| Model ID | deepseek-v4.1-flash | kimi-k3 |
Model card: what the makers publish
| Fact | DeepSeek V4.1 Flash | Kimi K3 |
|---|---|---|
| Released | Sep 10, 2026 | — |
| Made by | DeepSeek (DeepSeek-AI) | — |
| Licence | MIT License (repository and model weights) | — |
| Commercial use | Allowed (MIT License permits use, modification, distribution, sublicensing and sale) | — |
| Open weights | Yes | — |
| Parameters | 552B backbone parameters (includes 196B Engram conditional-memory parameters per the card's architecture notes) | — |
| Active parameters | 8B per token during prefill / 16B per token during decode | — |
| Mixture of experts | Yes: 1 shared + 384 routed experts per MoE layer, 6 routed experts active per token | — |
| Architecture | Causal Encoder-Decoder (CED): 40-layer Transformer = 20-layer causal encoder + 20-layer decoder; decoder global KV projected from final encoder states. Compressed Sparse Attention 2 (CSA2, Full/Reindex/Reuse layer modes, Hierarchical Sparse Indexer), FP4 main KV cache (E2M1, one E4M3 scale per 16 channels), ~890 bytes/token global KV cache (~1/4 of DeepSeek-V4-Flash), SWA Bounded Replay, Single-Pass mHC, Engram conditional memory (196B params), DSpark speculative decoding, DeepSeek-ViT vision encoder + 2-layer MLP projector. | — |
| Context window | 1,048,576 tokens | — |
| Max output | 384K (DeepSeek's own API, 'MAXIMUM: 384K') | — |
| Input | Text, Image | — |
| Output | Text | — |
| Reasoning | Thinking mode with continuously controllable reasoning effort (integer 1-100). DeepSeek API: thinking on by default (effort 'high'); toggle via {"thinking": {"type": "enabled"|"disabled"}}, effort via reasoning_effort low/high/max. In thinking mode temperature/penalties are ignored; reasoning returned in reasoning_content. | — |
| Tool calling | Yes | — |
| JSON output | JSON Output via response_format {'type':'json_object'} (prompt must mention 'json') | — |
| Recommended sampling | temperature 1.0, top_p 0.95 or 1.0, context_window 1M, max_tokens >= 256K | — |
| Training data | Pre-trained from scratch on a 45T-token multimodal corpus; context extended to 1M at 34T tokens | — |
| Other features | Chat Prefix Completion (Beta), FIM Completion (Beta, non-thinking mode only), Responses API and Anthropic-format API on DeepSeek's own endpoint; context caching (cache-hit pricing); vision | — |
Sources
- Released: DeepSeek V4.1 Flash
- Made by: DeepSeek V4.1 Flash
- Licence: DeepSeek V4.1 Flash
- Commercial use: DeepSeek V4.1 Flash
- Open weights: DeepSeek V4.1 Flash
- Parameters: DeepSeek V4.1 Flash
- Active parameters: DeepSeek V4.1 Flash
- Mixture of experts: DeepSeek V4.1 Flash
- Architecture: DeepSeek V4.1 Flash
- Context window: DeepSeek V4.1 Flash
- Max output: DeepSeek V4.1 Flash
- Input: DeepSeek V4.1 Flash
- Output: DeepSeek V4.1 Flash
- Reasoning: DeepSeek V4.1 Flash
- Tool calling: DeepSeek V4.1 Flash
- JSON output: DeepSeek V4.1 Flash
- Recommended sampling: DeepSeek V4.1 Flash
- Training data: DeepSeek V4.1 Flash
- Other features: DeepSeek V4.1 Flash
Try both with one key
Same endpoint, same key, both hosted in the USA. Change one word to switch models, in Claude Code, Cursor, Cline or your own code.
Get your API key# Ask either model the same thing curl https://fullsendai.com/v1/chat/completions \ -H "Authorization: Bearer $FULLSEND_API_KEY" \ -d '{"model": "deepseek-v4.1-flash", "messages": [...]}' # …then swap one word -d '{"model": "kimi-k3", "messages": [...]}'
Questions
Is DeepSeek V4.1 Flash or Kimi K3 better?
Kimi K3 grades 88.7 against 83.5 for DeepSeek V4.1 Flash, from public benchmarks.
Which is cheaper, DeepSeek V4.1 Flash or Kimi K3?
DeepSeek V4.1 Flash: $0.20 in and $0.60 out per million tokens, against $2.85 and $14.25 through FullsendAI.
Which has the larger context window?
Neither: both take 1,048,576 tokens.
Can I use both with one API key?
Kimi K3 is available now; the other is coming soon, at the price shown. Both will use the same FullsendAI key.
Compare with others
Scores: LMArena (CC BY 4.0), Terminal-Bench, and model makers’ published results where marked v. Prices are FullsendAI’s, per million tokens. Data as of Oct 28, 2026.