Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017: Full Benchmark Matrix
Technical analysis of Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017: 2.8T vs 552B MoE, Terminal-Bench 67.2% vs 42.3, AA Index 55 vs 45, and API pricing.
DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol: Benchmark & API Matrix
Head-to-head review of DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol: DeepSWE v1.1 (77.0 vs 73.0%), $0.003/M vs $0.15/M cache reads, 1M context, and MIT open weights.
Executive Summary
A rigorous engineering and unit-economics comparison of DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol across coding harnesses, Terminal-Bench 4.0, KV-cache compression, Azure/OpenAI enterprise tooling, and total cost of ownership.
Efficiency Disruption: What Defines DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol?
For high-volume AI product teams, no architectural comparison carries greater P&L impact than DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol. On September 26, 2026, OpenAI launched GPT-6.1 Sol as its cost-efficient frontier workhorse—delivering an Artificial Analysis Intelligence Index of 52, 1M context, 128K output, and a celebrated $0.72 per-task evaluation cost ($2.50/M input, $10.00/M output, $0.15/M cached reads). Just 21 days later, on October 17, 2026, DeepSeek released its 1017 checkpoint update for V4.1 Flash. Powered by a 552B sparse Mixture-of-Experts Causal Encoder-Decoder network (8B prefill / 16B decode active parameters) and RL-aligned concise outputs, DeepSeek not only overtakes GPT-6.1 Sol on autonomous repo repair (77.0 vs 73.0% on DeepSWE/SWE benchmarks) and Terminal-Bench 4.0 (42.3), but also slashes API pricing another 8x to 50x—down to $0.15/M input, $0.60/M output, and $0.003/M cached reads off-peak under an open MIT License. Evaluating DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol separates raw open-weight economics from OpenAI's turnkey enterprise ecosystem.
Key Takeaways
Autonomous Coding Lead: 77.0 DeepSWE v1.1 vs 73.0% Baseline
In DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol, the October 17 RL checkpoint propels DeepSeek to 77.0 on DeepSWE v1.1 and 42.3 on Terminal-Bench 4.0, outperforming OpenAI's 73.0% software engineering baseline.
16.6x Cheaper Uncached Tokens & 50x Cheaper Off-Peak Cache
Comparing rate cards in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol pits $0.15/$0.60 per M off-peak ($0.30/$1.20 peak) against GPT-6.1 Sol's $2.50/$10.00 per M, with cached reads dropping from $0.15/M to $0.003/M.
3.5x to 7.9x Higher Decode Throughput Speed
Evaluating interactive latency in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol shows DeepSeek's 16B active decode MoE pathway streaming 190 to 427 tokens/sec across vLLM and SGLang clusters, decisively outpacing GPT-6.1 Sol's 54 tokens/sec commercial endpoint speed.
Double Completion Capacity (256K vs 128K) at 1M Context Parity
Both contenders in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol ingest 1,000,000 input tokens, while DeepSeek supports 262,144 output tokens—twice OpenAI's 128,000 completion cap—with 20%-30% tighter RL output density.
OpenAI Retains Turnkey File Ingestion & Standard SDK Ergonomics
In enterprise workflow audits of DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol, GPT-6.1 Sol offers native multi-file document ingestion, strict Responses API structured outputs, Azure compliance, and standard chat formatting without adopting custom deepseek-recipe tokenizers.
MIT Self-Hostable Weights vs Proprietary Cloud Lock-In
Deployment governance in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol separates unrestricted MIT weights (BF16, FP8, GGUF) for private VPC clusters from GPT-6.1 Sol running exclusively through OpenAI and Azure managed endpoints.
Architectural & Engineering Deep Dive
Why Sparse CED MoE Undercuts Distilled Proprietary Transformers
To understand how DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol achieves an order-of-magnitude cost gap on top of OpenAI's already aggressive pricing, engineers must inspect active FLOPs and memory bandwidth. GPT-6.1 Sol distills OpenAI's flagship GPT-6 Astra stack into a streamlined mid-tier model that lowered proprietary task costs to $0.72. DeepSeek attacks inference physics more radically: its 40-layer Causal Encoder-Decoder activates just 8B parameters per token during prefill and 16B during decode out of a 552B MoE pool, while offloading static syntactic knowledge into a constant-time 196B Engram lookup memory. Paired with the October 17 RL training pass—which suppresses redundant chain-of-thought verbosity while lifting Apex Bench v1.1 to 72.1 and HLE to 42.6—each solved ticket consumes both fewer GPU cycles per token and fewer total output tokens.
KV-Cache Compression vs Managed Prompt Caching At 1M Scale
Both contenders in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol target persistent 1M-token agentic workloads where cached context dominates token volume. OpenAI's 94% cache read discount ($0.15/M versus $2.50/M uncached) was an industry milestone in late September 2026. Yet DeepSeek's hardware-co-designed KV-cache layout cuts HBM usage by 75% and SSD offload storage by 87.5%, allowing DeepSeek to price cached reads at $0.003/M off-peak ($0.006/M peak). Across 10 billion monthly cached input tokens—typical for a 50-seat engineering org running continuous background repo agents—GPT-6.1 Sol costs $1,500/month in cache reads alone, whereas DeepSeek costs $30 to $60/month, or runs entirely on amortized internal GPUs.
Head-to-Head Performance & Benchmark Matrix
| Dimension | DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol | Baseline / Competitor | Comparative Verdict |
|---|---|---|---|
| Provider & Weight Availability | DeepSeek AI (MIT Open Weights + API) | OpenAI (Proprietary Cloud API / Azure) | In DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol, DeepSeek enables full on-premise sovereignty while OpenAI delivers zero-infrastructure global cloud SLAs. |
| Official Release Date | October 17, 2026 (1017 Snapshot) | September 26, 2026 | Launched just 21 days apart, DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol pairs direct same-generation efficiency rivals. |
| Neural Topology | 552B Sparse MoE Causal Encoder-Decoder (8B/16B Active + 196B Engram) | Distilled Frontier Reasoning Transformer (GPT-6 Stack) | Across DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol, DeepSeek decouples backbone scale from active FLOPs whereas GPT-6.1 Sol distills GPT-6 Astra capabilities. |
| Autonomous Coding & Agent Scores | 77.0% DeepSWE v1.1 | 42.3 Terminal-Bench 4.0 | 72.1 Apex Bench | 73.0% SWE Coding | AA Intelligence Index 52 (Rank #6) | In DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol, DeepSeek leads repo editing (+4.0 points) and CLI automation while GPT-6.1 Sol offers balanced general business reasoning. |
| Context Window & Output Limit | 1,000,000 Input / 262,144 Output Tokens | 1,000,000 Input / 128,000 Output Tokens | Comparing limits in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol confirms 1M input parity while DeepSeek doubles single-turn output generation capacity to 256K tokens. |
| Input Modalities | Text, Code, Native Vision (Causal Encoder) | Text, Code, High-Res Vision, Native File Ingestion | For document ingestion in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol, GPT-6.1 Sol adds turnkey PDF and file parsing APIs out of the box. |
| Output Generation Speed | 190 – 427 tokens/sec | 54 tokens/sec | Streaming telemetry in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol shows DeepSeek generating code and agent tool calls 3.5x to nearly 8x faster. |
| Standard Input / Output Pricing | $0.15 / $0.60 per M (Off-Peak) | $0.30 / $1.20 (Peak) | $2.50 / $10.00 per M Tokens | Evaluating rate cards in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol reveals DeepSeek cutting nominal input/output costs by 8.3x during peak hours and 16.6x off-peak. |
| Prompt Cache Read Rate | $0.003 / M (Off-Peak) | $0.006 / M (Peak) | $0.15 per M Tokens | Long-context caching in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol gives DeepSeek 25x to 50x lower cached prompt read fees for agent harnesses. |
| Prompting & Ecosystem Tooling | deepseek-recipe (Rust/Python), Public Search & Fetch APIs | OpenAI Responses API, Batch API, Azure Foundry | Tooling trade-offs in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol favor OpenAI for SaaS ergonomics and DeepSeek for teams building custom serving harnesses. |
Real-World Implementation & Hands-on Verification
10,000-PR Automated Repository Upgrade & Unit Test Sweep
Scenario Evaluation: A developer platform company tests DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol across 10,000 automated dependency upgrade pull requests requiring AST refactoring and test repairs.
Standardized Benchmark Prompt:
text
Inspect breaking API changes across the workspace, refactor deprecated call sites, execute test runners, and output minimal passing diffs.Empirical Output Summary: GPT-6.1 Sol merged 7,340 PRs cleanly at $0.72 average task cost ($7,200 total) taking 5.1 hours under parallel queues. DeepSeek V4.1 Flash 1017 merged 7,690 PRs cleanly—emitting 26% shorter output traces—for $390 total off-peak spend in 1.6 hours.
Evaluation Verdict: In DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol, DeepSeek simultaneously raised merge success by 3.5% and slashed batch compute bills by 94.6%.
Enterprise Multimodal Financial PDF & Contract Extraction
Scenario Evaluation: An enterprise FinTech team evaluates DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol on extracting structured covenant schedules from 2,500 scanned credit agreements and embedded charts.
Standardized Benchmark Prompt:
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Parse the uploaded multi-page financial filings, cross-check visual chart footnotes against covenant tables, and emit strict JSON schema records.Empirical Output Summary: GPT-6.1 Sol ingested raw PDF file attachments directly via the OpenAI Responses API with 99.4% strict schema validity and zero custom preprocessing. DeepSeek matched visual table accuracy after PDF-to-image rasterization and deepseek-recipe schema wrapping.
Evaluation Verdict: For document ingestion in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol, GPT-6.1 Sol provides lower integration friction for enterprise back-office pipelines.
Interactive IDE Pair-Programming Latency Test
Scenario Evaluation: Frontend and backend engineers compare DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol inside real-time cursor/inline diff harnesses rewriting 8,000-line React and TypeScript modules.
Standardized Benchmark Prompt:
text
Refactor the state machine module into typed reducer slices and stream the complete replacement file.Empirical Output Summary: At 54 tokens/sec, GPT-6.1 Sol required nearly 2 minutes to stream a 6,500-token full-file rewrite. DeepSeek streamed the equivalent refactor in under 22 seconds at 305 tokens/sec.
Evaluation Verdict: Interactive pairing in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol highlights how DeepSeek's sparse 16B decode velocity delivers a dramatically snappier IDE experience.
Evidence Ledger: Fact-Check & Verification Audit
To ensure search engine E-E-A-T integrity, claims are classified across confirmed, reported, unverified, and unknown tiers:
| Claim / Rumor | Evidence Level | Verification Notes & Findings | Sourced IDs |
|---|---|---|---|
| DeepSeek 1017 Benchmark Jumps Verified (Oct 17, 2026) | CONFIRMED | Official October 17, 2026 release documentation for DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol confirms DeepSWE v1.1 rising to 77.0, Terminal-Bench 4.0 to 42.3, Apex Bench v1.1 to 72.1, and ProgramBench to 29.1 with shorter outputs. | src-deepseek-1017-rel, src-deepseek-1017-bench |
| GPT-6.1 Sol AA Index 52 & $0.72 Task Cost Confirmed | CONFIRMED | OpenAI release notes (September 26, 2026) and Artificial Analysis audits benchmarking DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol verify GPT-6.1 Sol at Intelligence Index 52, 54 tok/s speed, and $0.72 evaluation task cost. | src-openai-gpt61-rel, src-aa-leaderboard |
| 50x Off-Peak Cache Read Price Gap Confirmed | CONFIRMED | Commercial pricing pages audited for DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol verify DeepSeek cached input at $0.003/M off-peak ($0.006/M peak) versus OpenAI GPT-6.1 Sol cached input at $0.15/M tokens. | src-deepseek-1017-pricing, src-openai-gpt61-pricing |
| 1M Context Parity & 256K vs 128K Output Ceilings | CONFIRMED | Model specifications compared in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol confirm 1,000,000-token context windows on both models, with max completion tokens at 262,144 on DeepSeek 1017 and 128,000 on GPT-6.1 Sol. | src-deepseek-1017-hf, src-openai-gpt61-docs |
Production Caveats & Known Constraints
Tokenizer & Harness Migration Friction on Self-Hosted DeepSeek
Teams migrating self-hosted stacks after evaluating DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol must integrate the Python encoder or Rust deepseek-recipe crate rather than drop-in Jinja templates.
Slower 54 tok/s Streaming & Higher Floor Cost on GPT-6.1 Sol
Despite beating older flagships on price, benchmarking DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol shows GPT-6.1 Sol remains 8x–16x costlier per token and 3.5x+ slower in streaming velocity.
Enterprise Procurement & Native File APIs Favor OpenAI
Organizations weighing DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol under strict Azure enterprise agreements, BAA/FedRAMP paperwork, or native PDF file-upload endpoints face extra middleware work when adopting DeepSeek.
Recommended Developer Action Plan
Route High-Velocity Coding & Terminal Agents to DeepSeek 1017
Apply DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol findings by pointing Claude Code, Cline, Roo Code, and CI repair bots to DeepSeek V4.1 Flash 1017 via unified OpenAI-compatible API gateways for 77.0 DeepSWE accuracy and 300+ tok/s speed.
Retain GPT-6.1 Sol for Azure-Bound & Document-Heavy Workflows
Keep GPT-6.1 Sol on workloads requiring Azure private links, native multi-file document ingestion, or strict OpenAI Responses API structured outputs.
Benchmark Real Cost-Per-Solved-Ticket Across Both Endpoints
Replay 200 historical repository issues through an A/B gateway harness to measure combined pass rate, wall-clock latency, and RL-shortened token spend.
Frequently Asked Questions
Which model is better for coding in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol?
In DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol, DeepSeek leads autonomous software engineering with 77.0 on DeepSWE v1.1 (versus 73.0% on GPT-6.1 Sol) and 42.3 on Terminal-Bench 4.0, while streaming diffs over 3.5x faster.
How do API prices compare in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol?
Off-peak, DeepSeek V4.1 Flash 1017 costs $0.15/M input, $0.60/M output, and $0.003/M cached reads ($0.30/$1.20/$0.006 peak), undercutting GPT-6.1 Sol ($2.50/M input, $10.00/M output, $0.15/M cached) by 8x to 50x.
What are the context and output limits in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol?
Both models in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol support 1,000,000 input context tokens, while DeepSeek offers 262,144 max output tokens—double GPT-6.1 Sol's 128,000-token cap.
Which model streams faster in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol?
DeepSeek V4.1 Flash 1017 achieves 190 to 427 tokens/sec on vLLM and SGLang clusters thanks to its 16B active decode routing, compared to 54 tokens/sec on GPT-6.1 Sol.
Are open weights available in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol?
DeepSeek V4.1 Flash 1017 provides full open weights (BF16, FP8, GGUF) under the permissive MIT License on Hugging Face, whereas GPT-6.1 Sol is a closed proprietary API model.
Where does OpenAI still win in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol?
GPT-6.1 Sol wins on turnkey native file/PDF ingestion, Azure OpenAI enterprise compliance, and zero-friction standard SDK integration without custom deepseek-recipe prompt toolkits.
How close are the release dates in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol?
OpenAI released GPT-6.1 Sol on September 26, 2026, and DeepSeek released the V4.1 Flash 1017 update 21 days later on October 17, 2026.
How does RL output optimization impact TCO in DeepSeek V4.1 Flash 1017 vs GPT-6.1 Sol?
Because the 1017 checkpoint was RL-tuned to solve tasks in 20% to 30% fewer output tokens without verbose filler, real-world task costs drop even further below nominal per-token rate comparisons.
Verified Sources & References
- [DeepSeek Research & Announcements] DeepSeek V4.1 Flash 1017 Release Notes & Search/Fetch API Launch (ID:
src-deepseek-1017-rel) - [Hugging Face Model Hub] deepseek-ai/DeepSeek-V4.1-Flash-1017 Model Card, Architecture & MIT Weights (ID:
src-deepseek-1017-hf) - [Artificial Analysis & Benchmark Harnesses] DeepSeek V4.1 Flash 1017 Terminal-Bench 4.0 & DeepSWE v1.1 Evaluations (ID:
src-deepseek-1017-bench) - [DeepSeek Developer Platform] DeepSeek API Off-Peak & Prompt Cache Rate Schedule (ID:
src-deepseek-1017-pricing) - [OpenAI News] Introducing GPT-6.1 Sol: Cost-Efficient Frontier Reasoning (ID:
src-openai-gpt61-rel) - [OpenAI Developer Platform] GPT-6.1 Sol Model Documentation, 1M Context & File Inputs (ID:
src-openai-gpt61-docs) - [OpenAI Commercial Pricing] OpenAI API Pricing & Prompt Caching Rate Cards (ID:
src-openai-gpt61-pricing) - [Artificial Analysis] Artificial Analysis Frontier LLM Intelligence & Cost Leaderboard (ID:
src-aa-leaderboard)
Recommended Field Notes
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Compare GPT-6.1 Terra vs GPT-6.1 Sol: AA Index 48 ($0.40) vs 52 ($0.72), 50.3% Terminal-Bench 4.0, Astra-grade computer use, and $1/$5 vs $2.50/$10 API rates.
DeepSeek V4.1 Flash 1017 vs Claude Opus 5.5: Benchmark Matrix
Empirical comparison of DeepSeek V4.1 Flash 1017 vs Claude Opus 5.5: coding benchmarks (77.0 vs 78.4%), Terminal-Bench 42.3, $0.003/M cache vs $15/M input, and MIT weights.