DeepSeek
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the `deepseek-flash` API model name. V4.1 Flash is a sparse mixture-of-experts model...
Cost / 1M tokens
Low usage$0.15
input
$0.003
cached input
$0.60
output
Context Window
1M
Up to 393K max output
Architecture
552B total · 32B active MoE
text + image → text
Quickstart & API
deepseek/deepseek-v4.1-flash-1017Best for & Strengths
Key Strengths
Recommended Workloads in ChatOSS
- Autonomous coding agents
- Full-stack web app generation
- High-volume agent loops
Verified Evaluations
Benchmarks & Frontier Comparison
How DeepSeek V4.1 Flash 1017 compares against leading open-weight and proprietary frontier models across independent agentic coding and reasoning suites.
| Benchmark (Metric) | DeepSeek V4.1 Flash 1017 | GLM 5.3 | Kimi K3.1 Pro | Claude Opus 5.5 | Gemini 4 Argon |
|---|---|---|---|---|---|
| Agentic & Software Engineering | |||||
| ChatOSS Agentic Coding Index (%) | 63.2% | 58.3% | 65% | 74.1% | 73.9% |
| SWE-Bench Verified (%) | — | 95.4% | 94.8% | 97% | 94% |
| DeepSWE v1.1 (Pass@1 %) | 77% | 69% | 73.5% | 74.2% | 77.9% |
| Vibe Code Bench v1.1 (%) | 87.73% | 78.1% | 88.4% | 90.3% | 91.9% |
| Terminal-Bench 4.0 (%) | 42.3% | 42% | 46.2% | 65.2% | 57.6% |
| Code Migration (%) | 45.62% | 44.2% | 52% | 66.7% | 68.2% |
| Intelligence & Token Economics | |||||
| Artificial Analysis Intelligence Index (0–100) | 39.5 | 44.8 | 55.1 | 57.6 | 52.6 |
| Input Price (/ 1M tokens) | $0.15 | $1.40 | $2.00 | $4.00 | $4.00 |
| Output Price (/ 1M tokens) | $0.60 | $4.40 | $10.00 | $20.00 | $20.00 |
ChatOSS Agentic Coding Index
ChatOSS Benchmark Audit · 4-benchmark composite mean
DeepSWE v1.1
DeepSWE / Official Audit · Mini-SWE-Agent
Vibe Code Bench v1.1
Vals.ai · OpenHands
Terminal-Bench 4.0
Vals.ai / Artificial Analysis · Terminus 2
Terminal-Bench 2.1
Vals.ai · Terminus 2
Code Migration
Vals.ai · Vals evaluation harness
AutomationBench
Artificial Analysis v4.3
CyberGym (Pass@1)
Model Release Evaluation
Artificial Analysis Intelligence Index
Artificial Analysis
GPQA Diamond (Pass@1)
Benchmark Audit
Humanity's Last Exam (Pass@1)
Artificial Analysis / Audit
Design Arena: SPREADSHEET
Design Arena · Rank #6 (51.2% win)
Design Arena: 3D
Design Arena · Rank #10 (46.7% win)
Design Arena: ASCIIART
Design Arena · Rank #20 (50.8% win)
Design Arena: Code Generation
Design Arena · Rank #20 (47.8% win)
Active Provider Endpoints (36)
Upstream inference providers serving DeepSeek V4.1 Flash 1017, with per-provider pricing, context limits, quantization, and 24-hour uptime.
| Provider | Input / 1M | Output / 1M | Cache Read / 1M | Context | Max Output | Quantization | Uptime (24h) |
|---|---|---|---|---|---|---|---|
OpenInferencecache open-inference/fp4 | $0.018 | $1.80 | $0.017 | 1M | 944K | fp4 | 99.9% |
Rekacache reka | $0.04 | $0.50 | $0.004 | 1M | 393K | — | 96.2% |
Wafercache wafer/mxfp4 | $0.07 | $0.50 | $0.0069 | 1M | 944K | mxfp4 | 100.0% |
Relacecache relace | $0.07 | $0.50 | $0.008 | 1M | 944K | — | 99.8% |
DeepInfracache deepinfra/fp8/flex | $0.078 | $0.235 | $0.0024 | 1M | 131K | fp8 | 68.5% |
InferenceNetcache inference-net/us | $0.08 | $0.55 | $0.008 | 1M | 393K | fp8 | 100.0% |
InferenceNetcache inference-net/fp8 | $0.08 | $0.55 | $0.008 | 1M | 393K | fp8 | 100.0% |
DeepInfracache deepinfra/fp8 | $0.098 | $0.294 | $0.0029 | 1M | 131K | fp8 | 99.9% |
Cloudflarecache cloudflare | $0.10 | $0.60 | $0.003 | 1M | 944K | — | 100.0% |
Sail Researchcache sail-research/fp4/us | $0.101 | $0.15 | $0.0067 | 1M | 384K | fp4 | 100.0% |
Sail Researchcache sail-research/fp4 | $0.101 | $0.15 | $0.0067 | 1M | 384K | fp4 | 100.0% |
Morphcache morph/fp8 | $0.123 | $0.368 | $0.004 | 1M | 944K | fp8 | 100.0% |
Ionstreamcache ionstream | $0.129 | $0.51 | $0.0072 | 1M | 944K | — | 99.9% |
Decartcache decart/fp4 | $0.129 | $0.516 | $0.0026 | 1M | 262K | fp4 | 100.0% |
DeepInfracache deepinfra/fp8/priority | $0.147 | $0.441 | $0.0044 | 1M | 131K | fp8 | 100.0% |
DeepSeekcache deepseek | $0.15 | $0.60 | $0.003 | 1M | 393K | — | 100.0% |
Makoracache makora/fp8 | $0.15 | $1.20 | $0.006 | 1M | 944K | fp8 | 99.9% |
Alibabacache alibaba | $0.165 | $0.66 | $0.017 | 1M | 393K | — | 100.0% |
StreamLakecache streamlake/fp8 | $0.178 | $0.714 | $0.0036 | 1M | 384K | fp8 | 100.0% |
GMICloudcache gmicloud/fp8 | $0.18 | $0.72 | $0.0036 | 1M | 944K | fp8 | 100.0% |
AtlasCloudcache atlas-cloud/fp8 | $0.195 | $0.78 | $0.0039 | 1M | 393K | fp8 | 100.0% |
Novitacache novita/fp8 | $0.195 | $0.78 | $0.0039 | 1M | 393K | fp8 | 100.0% |
Phalacache phala | $0.20 | $1.20 | $0.006 | 1M | 393K | — | 99.9% |
Fireworkscache fireworks/spot | $0.21 | $0.84 | $0.0042 | 1M | 944K | — | 100.0% |
DigitalOceancache digitalocean | $0.225 | $0.90 | $0.006 | 1M | 944K | — | 100.0% |
Modalcache modal | $0.25 | $1.00 | $0.005 | 1M | 944K | — | 99.4% |
Baiducache baidu/fp8 | $0.28 | $1.13 | $0.006 | 1M | 393K | fp8 | 100.0% |
Crusoecache crusoe/fp8 | $0.29 | $1.20 | $0.007 | 1M | 944K | fp8 | 100.0% |
Nebiuscache nebius/fp8 | $0.30 | $1.20 | $0.006 | 1M | 944K | fp8 | 98.8% |
SiliconFlowcache siliconflow/fp8 | $0.30 | $1.20 | $0.006 | 1M | 393K | fp8 | 99.7% |
Togethercache together | $0.30 | $1.20 | $0.006 | 1M | 944K | — | 100.0% |
Fireworkscache fireworks | $0.30 | $1.20 | $0.006 | 1M | 944K | — | 98.3% |
PrimeIntellectcache primeintellect | $0.30 | $1.20 | $0.006 | 1M | 131K | — | 99.9% |
BaseTencache baseten | $0.30 | $1.20 | $0.007 | 1M | 262K | — | 100.0% |
Parasailcache parasail/fp8 | $0.30 | $1.20 | $0.006 | 1M | 944K | fp8 | 99.9% |
Fireworkscache fireworks/us | $0.45 | $1.80 | $0.009 | 1M | 944K | — | 100.0% |
Supported API Parameters
Parameters accepted by this model when called through ChatOSS or OpenRouter.
Deep-Dive Benchmarks & Teardowns
Independent benchmark analysis and real-world agent billing teardowns featuring DeepSeek V4.1 Flash 1017.
2026-10-27 · 10 min read
MiniMax M3.1 Flash vs. DeepSeek V4.1 Flash 1017: cheaper tokens, higher bill
Both list at $0.15/$0.60 per million tokens, and MiniMax wins the business-workflow benchmarks. But on vals.ai's Vibe Code Bench v1.1, DeepSeek's October 17 checkpoint scores 87.73% at $0.58 per test while M3.1 Flash scores 73.94% at $1.15 — because the cheaper token burns 66.86M of them per task. Here is the independent evidence and the cache-aware math.
2026-09-21 · 11 min read
GLM 5.3 Flash vs. DeepSeek V4.1 Flash: which is better at agentic coding?
Both labs ship a million-token context, MIT weights, and a ~$0.15-per-million price tag. On the independent harnesses that have actually run both models — vals.ai's Vibe Code Bench and Terminal-Bench 2.1, BenchLM's Vibe Code Bench 1-100, and Artificial Analysis' v4.3 index — DeepSeek V4.1 Flash wins agentic coding, and by a wider margin than either vendor's own table suggests.
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