GPT-6.1 Terra vs GPT-6.1 Sol: Benchmarks, Pricing & Routing
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.
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.
Executive Summary
Empirical architectural guide to Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 covering 104B vs 8B/16B active routing, Terminal-Bench 4.0 and DeepSWE v1.1 harnesses, 1M caching, 256K outputs, and open weights.
Architectural Clash: What Defines Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017?
Open-weight engineering split into two scaling philosophies within six days of October 2026 in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017. On October 17, 2026, DeepSeek shipped its 1017 checkpoint: a 552B Causal Encoder-Decoder (CED) MoE activating 8B prefill and 16B decode parameters plus a 196B Engram lookup memory, posting 77.0% on DeepSWE v1.1, 42.3 on Terminal-Bench 4.0, AA Index 45, 190–427 tok/s, and $0.15/$0.60 off-peak pricing ($0.003/M cached) with live MIT weights. Six days later, on October 23, 2026, Moonshot AI launched its 2.8T MoE (104B active) flagship, hitting AA Index 55 (#4 globally, $2.11 task cost), 65.7%–67.2% Terminal-Bench 4.0, 52% HLE, 73% AutomationBench-AA, and 92.7% BenchCAD at $2.00/$10.00 ($0.20/M cached), with open weights due November 4, 2026. Evaluating Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 contrasts peak per-token deduction against radical FLOP and cache frugality.
Key Takeaways
10-Point Intelligence & 24.9-Point Terminal Lead for Moonshot
In Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017, Moonshot's 104B active path scores AA Index 55 (#4 globally), 67.2% Terminal-Bench 4.0, and 52% HLE—beating DeepSeek's 45 AA Index, 42.3 Terminal-Bench, and 42.6% HLE.
8x Lower End-to-End Task Cost & 66x Cheaper Cache on DeepSeek
Cost telemetry for Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 shows a $0.26 AA task cost and $0.003/M off-peak cache on DeepSeek versus $2.11 task cost and $0.20/M cache on Moonshot.
2.8T Classic Sparse MoE vs 552B Asymmetric CED + 196B Engram
Comparing parameter graphs in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 pits 2.8T total / 104B active weights against 552B total / 8B prefill & 16B decode weights with 196B Engram syntax memory.
DeepSWE v1.1 (77.0%) Conciseness vs BenchCAD (92.7%) & Automation (73%)
In Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017, DeepSeek shines on GitHub bug patches (77.0% DeepSWE v1.1, 20%–30% shorter RL outputs), while Moonshot leads 73% AutomationBench-AA and 92.7% BenchCAD.
Shared 1M Context Window, Doubled 256K Output Ceiling on DeepSeek
Both models in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 ingest 1,000,000 multimodal tokens, while DeepSeek emits up to 262,144 completion tokens—double Moonshot's 128,000-token cap.
Live Single-Node MIT Weights vs November 4 Multi-Node Flagship
Deployment in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 contrasts DeepSeek's live MIT weights on one 8x H200 FP8 node (190–427 tok/s) against Moonshot's November 4, 2026 multi-node 2.8T weights.
Architectural & Engineering Deep Dive
Scaling Philosophy: 2.8T / 104B Active Width vs 552B CED + Engram
At the transformer core, Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 is an architectural referendum on per-token active width. Moonshot AI scales to 2.8T total parameters and activates 104B on every token, preserving wide working memory across system calls, formal proofs (52% HLE, 61% SciCode), and 3D geometry (92.7% BenchCAD). DeepSeek's 552B 40-layer Causal Encoder-Decoder splits into 20 encoder layers activating 8B parameters during prefill and 20 decoder layers activating 16B during generation, offloading static syntax into a 196B Engram lookup memory while RL alignment trims 20%–30% of verbose tokens to hit 190–427 tok/s.
Agentic Harness Divergence: Terminal-Bench 4.0 vs DeepSWE v1.1
Why does Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 show Moonshot ahead by ~25 points on Terminal-Bench 4.0 (65.7%–67.2% vs 42.3) while DeepSeek hits 77.0% on DeepSWE v1.1? DeepSWE v1.1 inside Mini-SWE-V2 centers on localizing code bugs and generating diffs—where DeepSeek's 16B decoder and 196B Engram excel. Conversely, Terminal-Bench 4.0 and AutomationBench-AA (73% on Moonshot) inject noisy daemon failures, broken toolchains, and multi-app workflows in Kimi Work v3.1.0+ and Kimi Code, where Moonshot's 104B active width recovers cleanly from unexpected stderr states.
1M Context Cache Math, Dual-Speed Routing & Cluster Topology
Both models in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 expose 1,000,000-token windows, yet target distinct gateway tiers. DeepSeek compresses KV-cache HBM by 75% and SSD by 87.5%, enabling $0.003/M off-peak cached reads and a $0.26 AA task cost for high-concurrency CI sweeps. Moonshot prices cached reads at $0.20/M across standard ($2/$10) and Highspeed ($4/$20, 2x–3x faster) endpoints for a $2.11 AA task cost. Self-hosted teams can run DeepSeek's live MIT weights on one 8x H200 FP8 server today or provision 3–4 H200/B200 nodes for Moonshot's November 4 open weights.
Head-to-Head Performance & Benchmark Matrix
| Dimension | Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 | Baseline / Competitor | Comparative Verdict |
|---|---|---|---|
| Release Date & Open-Weight Availability | Oct 23, 2026 GA | Open Weights by Nov 4, 2026 | Oct 17, 2026 GA | MIT Open Weights Live on HF | Launched 6 days apart, Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 pairs live MIT downloads against Moonshot's Nov 4 release. |
| Total & Active MoE Parameter Topology | 2.8T Sparse MoE (104B Active / Token) | 552B CED MoE (8B Prefill / 16B Decode + 196B Engram) | In Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017, Moonshot routes 6.5x–13x more active parameters per token for deep deduction. |
| Artificial Analysis Intelligence Index & Task Cost | AA Index 55 (Rank #4 Global) | $2.11 Task Cost | AA Index 45 | $0.26 Task Cost | In Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017, Moonshot leads intelligence by 10 points while DeepSeek cuts task spend by 8.1x. |
| Terminal-Bench 4.0 Execution Accuracy | 65.7% – 67.2% (Up from 13% in K3) | 42.3% (Claude Code Harness, up from 31.2%) | CLI benchmarks in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 give Moonshot a decisive ~24.9-point lead on Linux shell recovery. |
| SWE, Automation & Spatial Tool Benchmarks | 73% AutomationBench-AA | 61% SciCode | 92.7% BenchCAD | 77.0% DeepSWE v1.1 | 20%–30% Shorter RL Outputs | DeepSeek excels at concise GitHub patches; Moonshot dominates enterprise automation and 3D CAD tool loops. |
| Humanity's Last Exam (HLE) Abstract Reasoning | 52.0% HLE | 42.6% HLE | Evaluating academic proofs in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 gives Moonshot's 104B active graph a +9.4-point edge. |
| Context Horizon & Maximum Output Ceiling | 1,000,000 Input / 128,000 Output Tokens | 1,000,000 Input / 262,144 Output Tokens (4x KV Compression) | Context parity at 1M tokens in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017, while DeepSeek doubles output capacity to 256K. |
| Generation Speed & Serving Tiers | Standard + Highspeed API (2x–3x Faster) | 190 – 427 tokens/sec (vLLM / SGLang) | In Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017, DeepSeek hits 427 tok/s while Highspeed accelerates Moonshot's 104B stream 2x–3x. |
| Uncached Input / Output API Pricing | $2.00 / $10.00 (Standard) | $4.00 / $20.00 (Highspeed) | $0.15 / $0.60 (Off-Peak) | $0.30 / $1.20 (Peak) | Rate cards in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 favor DeepSeek by 13.3x on input and 16.7x on output off-peak. |
| Prompt Cache Rate & Self-Hosting Hardware | $0.20 / M Cache | Multi-Node Cluster (2.8T) | $0.003 / M Off-Peak Cache | Single 8x H200 Node (552B) | Cache reads in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 favor DeepSeek by 66.7x alongside 5x lower cluster VRAM. |
Real-World Implementation & Hands-on Verification
Stateful Linux Cluster Recovery (Terminal-Bench 4.0)
Scenario Evaluation: An SRE squad tests Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 across 60 broken Kubernetes CNI, systemd, and RocksDB container sandboxes.
Standardized Benchmark Prompt:
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Diagnose packet drops and lockups in the live shell, repair config drift without data loss, and pass health checks.Empirical Output Summary: Moonshot resolved 40/60 incidents (66.7%) via multi-turn eBPF diagnostics at $2.05/ticket. DeepSeek 1017 resolved 26/60 (43.3%) with 25% shorter RL traces at $0.19/ticket.
Evaluation Verdict: In Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017, Moonshot earns its premium whenever stateful shell recovery demands 67.2%-class accuracy.
High-Volume GitHub PR Repair & 200K Test Synthesis
Scenario Evaluation: A platform team evaluates Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 on 200 TypeScript/Python bug PRs plus a single-pass 190,000-token test suite rewrite.
Standardized Benchmark Prompt:
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Isolate the regression across the 500K-token repo, emit a minimal unified diff, and generate an unchunked end-to-end fixture file.Empirical Output Summary: DeepSeek resolved 76.5% of PRs at 310 tok/s and emitted the 190K-token suite in one call (above Moonshot's 128K cap) using $0.003/M cache. Moonshot solved the hardest cross-service escalations.
Evaluation Verdict: Tiered routing in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 achieved 91% combined resolution under $0.55 per PR.
3D CAD Reconstruction vs Single-Node VPC Hosting
Scenario Evaluation: An industrial lab benchmarks Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 on raster-to-CAD bracket reconstruction and single-node 8x H200 air-gapped deployment.
Standardized Benchmark Prompt:
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Convert the orthographic blueprint image into watertight CadQuery STEP geometry and verify single-node FP8 serving.Empirical Output Summary: Moonshot produced watertight STEP geometry on the first tool render (matching its 92.7% BenchCAD score), while DeepSeek's MIT FP8 weights booted immediately on a single 8x H200 server.
Evaluation Verdict: In Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017, Moonshot owns spatial CAD tooling while DeepSeek owns single-node VPC hosting.
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 |
|---|---|---|---|
| Six-Day October 2026 Launch Window & Weight Licensing Timeline | CONFIRMED | Release records for Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 confirm DeepSeek launched October 17, 2026 with live MIT Hugging Face weights, while Moonshot AI launched October 23, 2026 with open weights scheduled by November 4, 2026. | src-kimi-k31-rel, src-deepseek-1017-rel, src-deepseek-1017-hf |
| 2.8T (104B Active) vs 552B CED (8B/16B Active + 196B Engram) Specs | CONFIRMED | Model architecture disclosures in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 verify Moonshot's 2.8T MoE / 104B active / 1M context / 128K output alongside DeepSeek's 552B CED MoE / 8B–16B active / 1M context / 262,144 output. | src-kimi-k31-docs, src-deepseek-1017-hf |
| AA Index (55 vs 45), Terminal-Bench 4.0 (67.2% vs 42.3) & DeepSWE (77.0%) | CONFIRMED | Independent leaderboards for Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 confirm AA Index 55 (#4, $2.11 cost), Terminal-Bench 65.7%–67.2%, HLE 52%, AutomationBench-AA 73%, and BenchCAD 92.7% vs DeepSeek's AA Index 45 ($0.26 cost), Terminal-Bench 42.3, HLE 42.6%, and DeepSWE v1.1 77.0%. | src-aa-leaderboard, src-tbench-swe |
| API Rate Card & Cache Differential ($2/$10/$0.20 vs $0.15/$0.60/$0.003) | CONFIRMED | Published pricing docs for Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 verify Moonshot Standard ($2/$10, $0.20 cache) and Highspeed ($4/$20, $0.20 cache) against DeepSeek off-peak ($0.15/$0.60, $0.003 cache) and peak ($0.30/$1.20, $0.006 cache). | src-kimi-k31-docs, src-deepseek-1017-pricing |
Production Caveats & Known Constraints
Steep Multi-Node VRAM Barrier on Moonshot's 2.8T Checkpoint
Self-hosting the Moonshot side of Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 after November 4 requires ~5x the cluster HBM of DeepSeek's 552B model, ruling out single-node on-premise deployments.
Terminal & Abstract Reasoning Ceiling on DeepSeek's 16B Decode Path
In Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017, DeepSeek trails by ~24.9 points on Terminal-Bench 4.0 and 9.4 points on HLE when tasks demand broad simultaneous world knowledge rather than localized code edits.
128K Output Cap & Custom Tokenizer Trade-Offs
Evaluating limits in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 notes Moonshot's 128,000-token output cap (half of DeepSeek's 262,144) versus DeepSeek's custom deepseek-recipe tokenizer requirement.
Recommended Developer Action Plan
Implement Two-Tier Open-Ecosystem Gateway Routing
Deploy a hybrid router guided by Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 telemetry: send high-volume SWE patches and >128K synthesis to DeepSeek 1017, and escalate shell incidents and CAD tooling to Moonshot.
Match Serving Endpoints to Human vs Batch Latency Profiles
Optimize latency in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 by binding Kimi Code / Kimi Work v3.1.0+ to Highspeed ($4/$20) and scheduling batch CI suites on DeepSeek off-peak windows ($0.003/M cache).
Align Private GPU Procurement to 552B vs 2.8T Cluster Tiers
Deploy DeepSeek's live MIT FP8 weights immediately on single 8x H200 nodes, or size multi-node H200/B200 fabrics ahead of Moonshot's November 4, 2026 open-weight release.
Frequently Asked Questions
Which model wins overall in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017?
In Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017, Moonshot wins peak reasoning (AA Index 55 vs 45), Terminal-Bench 4.0 (67.2% vs 42.3), HLE (52% vs 42.6%), and BenchCAD (92.7%), while DeepSeek wins cost ($0.26 vs $2.11), 256K outputs, tok/s, and single-node MIT hosting.
How do parameter counts differ in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017?
Comparing architectures in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 pits Moonshot's 2.8T sparse MoE (104B active) against DeepSeek's 552B 40-layer CED MoE (8B prefill / 16B decode + 196B Engram memory).
How do coding and terminal scores compare between the two models?
Agentic benchmarks in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 show Moonshot leading Terminal-Bench 4.0 (65.7%–67.2% vs 42.3) and AutomationBench-AA (73%), while DeepSeek posts 77.0% on DeepSWE v1.1 with 20%–30% shorter RL outputs.
What is the API price difference in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017?
Pricing in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 sets Moonshot at $2/$10 ($4/$20 Highspeed) and $0.20/M cache versus DeepSeek off-peak at $0.15/$0.60 and $0.003/M cache ($0.30/$1.20/$0.006 peak).
Are open weights available for both Kimi K3.1 Pro and DeepSeek V4.1 Flash 1017?
In Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017, DeepSeek's MIT weights launched on Hugging Face on October 17, 2026, while Moonshot scheduled open-weight publication by November 4, 2026.
Which model supports longer context and larger single-call completions?
Both models in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 ingest 1,000,000 text-and-image tokens, while DeepSeek doubles max output to 262,144 tokens versus Moonshot's 128,000.
How does inference speed compare in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017?
Throughput in Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 reaches 190–427 tok/s on DeepSeek's 16B decoder, while Moonshot's kimi-k3.1-pro-highspeed tier accelerates 104B generation 2x–3x.
How should engineering teams combine both models in production?
Best practice for Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 is tiered routing: run routine SWE diffs and 256K test generation on DeepSeek 1017, and route hard Linux shell incidents and 3D CAD tasks to Moonshot.
Verified Sources & References
- [Moonshot AI Official Blog] Kimi K3.1 Pro & Highspeed Release Notes, 2.8T MoE Architecture & Open-Weight Roadmap (ID:
src-kimi-k31-rel) - [Moonshot AI Developer Platform] Kimi K3.1 Pro & Highspeed API Rate Cards, Caching & 1M Context Specifications (ID:
src-kimi-k31-docs) - [DeepSeek Research & Announcements] DeepSeek V4.1 Flash 1017 Release Announcement: RL Density Alignment & Agentic Gains (ID:
src-deepseek-1017-rel) - [Hugging Face Model Hub] deepseek-ai/DeepSeek-V4.1-Flash-1017 Model Card, 552B CED MoE & MIT Weights (ID:
src-deepseek-1017-hf) - [DeepSeek Developer Platform] DeepSeek Commercial API Pricing, Off-Peak Windows & $0.003/M Prompt Caching (ID:
src-deepseek-1017-pricing) - [Artificial Analysis] Artificial Analysis Frontier Intelligence Index, AutomationBench-AA & Task Cost Matrix (ID:
src-aa-leaderboard) - [Terminal-Bench & SWE Evaluation Harnesses] Terminal-Bench 4.0, DeepSWE v1.1 & Humanity's Last Exam Comparative Results (ID:
src-tbench-swe)
Recommended Field Notes
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