21+ AI Labs. 95+ Models.
One Private Platform.

OpenAI's smartest model (released Apr 23, 2026).

OpenAI's most capable model and the top of the GPT-5.6 family (released Jul 2026).

OpenAI's flagship September 2026 frontier model with a 1.05M context window, MoE architecture, and SOTA reasoning across coding, formal mathematics, and long-horizon agentic automation.

Premium creative and analytical work

Creative writing, complex UI/UX coding, architectural decisions, production debugging, strategic advising, deep conceptual reasoning, emotional support

Successor to Opus 4.6 at the same price (released Apr 16, 2026).

Anthropic's most capable model (released May 28, 2026; knowledge cutoff Jan 2026).

Anthropic's flagship 1M-context frontier multimodal foundation model, engineered for deep reasoning, autonomous coding agents, and complex long-horizon system execution.

Multi-step deep research investigations

Legacy large-context analysis

Gemini 3.1 Flash TTS is a neural speech synthesis model by Google, providing expressive voice rendering for real-time conversation.

Abstract reasoning puzzles, competitive algorithms, agentic tool orchestration, scientific coding, large document analysis (10K+ tokens), technical/academic writing

Gemini 3.6 Flash is a high-performance foundation model engineered by Google, accessible with Zero Data Retention on ARMES.

Google's former workhorse model (released Aug 13, 2026; knowledge cutoff Mar 2026).

Legacy flagship

General-purpose intelligence

Strong general reasoning

Conversational variant of GPT-5.1

PhD-level math, advanced science, security audits, formal proofs

Conversational variant of GPT-5.2

Conversational AI with dramatically reduced hallucination rates, especially in medicine/law/finance.

OpenAI's most capable model.

GPT-5.4-mini is a high-performance foundation model engineered by OpenAI, accessible with Zero Data Retention on ARMES.

The speed and throughput tier of the GPT-5.6 family.

The value leader of the GPT-5.6 family.

SWE-bench Verified: 78.0%, GPQA Diamond: 82.7%, MMLU-Pro: 83.7%, AIME 2025: 90.2%, IFBench: 72.6%, SciCode: 42.0%, Terminal-Bench Hard: 31.0%, τ²-Bench Telecom: 64.4%, HLE: 22.8–30.0%, non-hallucination: ~78% (Thinking mode) | xAI's large-context multi-agent workhorse (beta Feb 17, 2026; GA Mar 10, 2026).

CaseLaw v2: 79.3% (#1, +25 pts over 4.20), CorpFin: #1, τ²-Bench Telecom: 97.7%, GDPval-AA: 1500 ELO, GPQA Diamond: 90.1%, IFBench: 81.0%, SciCode: 47.3%, Coding Index: 41–42.2%, Terminal-Bench Hard: 38.0%, HLE: 35.0%, Artificial Analysis Intelligence Index: 53 | xAI's most capable reasoning model (beta Apr 17, 2026; GA Apr 30, 2026).

Artificial Analysis Intelligence Index: 54 (#4 of 168), AA Coding Agent Index: 76 (on par with GPT-5.5), τ³-Banking: 33% (#1 of 28 models), GPQA Diamond: 93%, Terminal-Bench 2.1: 82–83.3%, SWE-Bench Pro: 64.7%, DeepSWE 1.0: 62%, SWE Marathon: 29% (beats Opus 4.8's 26%), ~15,954 output tokens per SWE-Bench Pro task (4.2× more token-efficient than Opus 4.8) | xAI's flagship model for software engineering, agentic execution, and technical reasoning (released Jul 8, 2026).

AA Intelligence Index: 61 (matches GPT-5.6 Sol), GDPval-AA v2: 1753 Elo, CursorBench v3.2: 69.9%, DeepSWE v1.1: 65.9%, FrontierCode v1.1 Extended: 61.3%, APEX-Agents: 57.5%, Terminal-Bench v3.0: 26%, APEX-SWE: 56.4%, AA-Briefcase: 1577 Elo, Harvey LAB: 15.8%, SWE-bench Verified (Vals): 95.60%, GPQA Diamond: 94.9%, HLE: 42.9%, AA Coding Index: 76.8, AA Agentic Index: 58.7, AA Non-Hallucination Rate: 65.7% | xAI's current frontier model (released Aug 12, 2026; knowledge cutoff Feb 1, 2026).

Anthropic's high-speed, frontier-class efficient model delivering 73.3% SWE-bench Verified coding performance at $1/$5 per million tokens with extended thinking capabilities.

GDPval-AA v2: 1668 Elo (#3; Fable 5: 1760, Sol: 1748, Opus 4.8: 1600), AA-Briefcase: 1548 Elo (#2; Fable 5: 1583, Sol: 1495), BrowseComp: 91.2% (#1; Sol: 90.4), Terminal-Bench 2.1: 88.3% (#2; Sol: 88.8), DeepSWE: 67.5% (#3; Sol: 73.0, Fable 5: 70.0), FrontierSWE: 81.2% (#2; Fable 5: 86.6), SWE Marathon: 42.0% (#1; Opus 4.8: 40.0), Program Bench: 77.8% (#1; Sol: 77.6), Automation Bench: 30.8% (#1; Sol: 29.7), SpreadsheetBench 2: 34.8% (#1; Fable 5: 34.7), JobBench: 52.9% (#2; Fable 5: 57.4), CharXiv (RQ) w/ tool: 91.3%, Zerobench w/ tool: 41.0%, Kimi Code Bench 2.0 (internal): 72.9%.

Microsoft: MAI-Voice-2-Flash is a neural speech synthesis model by Microsoft, providing expressive voice rendering for real-time conversation.

Nano Banana 2 Flash is an advanced image synthesis engine by Google, offering high-fidelity photorealistic rendering and prompt adherence.

Nano Banana 2 Lite is an advanced image synthesis engine by Google, offering high-fidelity photorealistic rendering and prompt adherence.

Nano Banana 2 Pro is an advanced image synthesis engine by Google, offering high-fidelity photorealistic rendering and prompt adherence.

ByteDance's dedicated coding model (released Feb 14, 2026 on Volcano Engine; Jul 30, 2026 on OpenRouter).

The production-optimized variant of Seed 2.1, built for high-throughput enterprise agent tasks (released Jun 23, 2026).

Seedream 4.5 is an advanced image synthesis engine by ByteDance, offering high-fidelity photorealistic rendering and prompt adherence.

Seedream 5.0 Lite is an advanced image synthesis engine by ByteDance, offering high-fidelity photorealistic rendering and prompt adherence.

Seedream 5.0 Pro is an advanced image synthesis engine by ByteDance, offering high-fidelity photorealistic rendering and prompt adherence.

Sesame: CSM 1B is a neural speech synthesis model by Sesame, providing expressive voice rendering for real-time conversation.

Research queries requiring cited sources

Web-augmented research with live search

Strong all-around coding and analysis

Coding, debugging, knowledge work, financial/legal reasoning, business writing, translation, data analysis, research.

Anthropic's most agentic Sonnet yet (released Jun 30, 2026; knowledge cutoff Jan 2026).

Legacy cost-efficient reasoning

Mathematics, science, PhD-level reasoning, data analysis, financial analysis, technical docs, competitive algorithms.

Sustained frontier-level intelligence for agentic execution, iterative coding cycles, multi-step tool use, finance analysis, and professional knowledge work — at Flash speed.

Gemini 3.5 Flash-Lite is a high-performance foundation model engineered by Google, accessible with Zero Data Retention on ARMES.

Google's next-generation workhorse model (released Sep 2, 2026; codenamed "Skimaki").

Standard coding and analysis

Agentic coding, complex planning, knowledge-intensive tasks

GLM-5.1 is a high-performance foundation model engineered by Z.ai, accessible with Zero Data Retention on ARMES.

Long-horizon agentic engineering, repository-scale refactors, multi-hour autonomous coding, cross-file/long-chain tasks, frontend coding (best-in-class among open models), tool use, and competition math.

Z.ai's most capable model (released Aug 14, 2026).

SWE-bench Pro: 65.7%, GPQA Diamond: 92.3%, Terminal-Bench 2.1: 85.4%, DeepSWE v1.1: 64.3%, HLE: 55.4%, SWE-bench Verified: ~82.9%, NL2Repo: 58.9%.

AIME 2026: 97.1%, HLE w/ Tools: 53.2%, GPQA Diamond: ~90%, competitive with Nemotron 3 Ultra, GLM 5.2, and DeepSeek V4 Pro across text/agentic/multimodal evaluations.

— | Previous generation research and reasoning

GPQA Diamond: 87.6%, SWE-bench: 76.8%, HLE-Full w/ Tools: 50.2% (#1), BrowseComp Swarm: 78.4%, TerminalBench: 50.8% | Research with tools, complex multi-step tasks, agentic workflows, strategic reasoning, document analysis, philosophy, deep conceptual work.

SWE-Bench Pro: 58.6 (leading), GPQA Diamond: 90.5%, HLE-Full w/ Tools: 54.0, MathVision (w/ Python): 93.2, GDPval-AA: 1520 Elo (vs 1309 for K2.5), Hallucination rate: 39% (vs K2.5's 65%) | Long-horizon agentic coding, autonomous engineering, full-stack workflows, persistent 24/7 background agents, multi-step DevOps.

(vendor-reported, in-house suites) Kimi Code Bench v2: 62.0 (+21.8% vs K2.6's 50.9; GPT-5.5 69.0, Opus 4.8 67.4), Program Bench: 53.6 (vs K2.6 48.3), MLS Bench Lite: 35.1 (+31.5% vs K2.6 26.7), Kimi Claw 24/7 Bench: 46.9, MCP Atlas: 76.0 (vs K2.6 69.4), MCP Mark Verified: 81.1 (vs K2.6 72.8) | Cost-efficient long-horizon agentic coding — multi-file refactors, hours-long autonomous engineering, CI/CD, large-codebase analysis across Rust/Go/Python/frontend/DevOps.

SWE-Bench Pro: 57.2%, Artificial Analysis Intelligence Index: 54, Claw-Eval Multimodal: 23.8 (matches Sonnet 4.6), Video-MME: 87.7 (rivals Gemini 3 Pro), HLE: 48.0%, autonomous workflows with 1,000+ tool calls | Complex software engineering, long-horizon agentic tasks, real-time video/audio analysis, multimodal chart interpretation, multi-step automated workflows.

SWE-Bench Verified: 70.7–71.9%, PinchBench: 90.0%, RULER @1M: 94.7%, LiveCodeBench v6: 89.0%, IOI 2025: 570, GPQA (no tools): 87.0%, IFBench: 81.7%, IMOAnswerBench: 88.6–92.3%, Terminal-Bench 2.1: 56.4%, Artificial Analysis Intelligence Index: 48 (highest US open model) | NVIDIA's most capable model (released Jun 4, 2026).

Terminal-Bench 2.1: 86.6% (vendor) / 81.3% (AA independent), SWE-bench Pro: 67.7%, DeepSWE 1.1: 56.6%, PaperBench: 93.0% (#1), GPQA Diamond: 92.6%, FrontierSWE: 73.5%, OSWorld-Verified: 86.1%, OmniDocBench 1.5: 92.1%, IFBench: 82.8%, HLE: 43.6%, JobBench: 53.4% | Alibaba's most capable model and the first Qwen-Max released as open weights (GA Aug 3, 2026; open weights Aug 12, 2026).

— | Deep reasoning, chain-of-thought tasks

V4 Pro is a high-performance foundation model engineered by DeepSeek, accessible with Zero Data Retention on ARMES.

Terminal-Bench 2.1: 87.9%, DeepSWE: 62.7%, CyberGym: 83.3%, AutomationBench: 31.8%, Toolathlon-Verified: 74.1%, HLE w/ tools: 60.0%, GPQA Diamond: 90.1%, Artificial Analysis Intelligence Index: 53.2, AA Coding Index: 68.8, AA Agentic Index: 49.6 | The official GA release of V4 Pro, superseding the April preview (released Aug 13, 2026).

— | Factual precision, low-hallucination tasks, rigorous analytical work

Mathematics, science, data analysis, financial reasoning, tech docs, academic writing, logic puzzles, document summarization.

Zhipu AI's 320B/18B-active multimodal MoE model featuring hybrid linear attention, 1.31M context, and top-tier agentic coding efficiency.

Legacy cost-efficient

Legacy ultra-lightweight

Cost-efficient general tasks

Ultra-lightweight, fastest OpenAI model

GPT-5.4-nano is a high-performance foundation model engineered by OpenAI, accessible with Zero Data Retention on ARMES.

GPQA Diamond: 90.4%, HLE: 53.2%, BrowseComp: 84.2% (beats DeepSeek V4-Pro 83.4 & Opus 4.7 79.3; ~ties GPT-5.5 84.4), DeepSearchQA: 91.0, MCP-Atlas: 79.1 (beats Opus 4.7's 77.3), Terminal-Bench 2.1: 71.7% (beats Opus 4.7 & DeepSeek V4-Pro), SWE-bench Verified: 78.0%, GSM8K: 95.37%, MATH: 76.28%, Factual hallucination rate: 5.4% (down from 12.5% in preview), MRCR long-dialogue: 75.1% (up from 42.9%) | Grounded factual Q&A and RAG (trained to answer when grounded and flag missing evidence rather than fabricate), web-browsing/research agents, multi-step tool orchestration, customer-support and long multi-turn dialogue (coreference resolution, constraint tracking across turns), professional document generation, focused coding tasks.

Inkling Small is a high-performance foundation model engineered by Thinking Machines, accessible with Zero Data Retention on ARMES.

Strong creative quality via Behemoth co-distillation | Creative writing, storytelling, business communications, emotional support, brainstorming, marketing copy, brand voice, heartfelt content.

MMLU: 79.6, MMLU-Pro: 74.3, GPQA Diamond: 57.2, MMMU: 73.4, MathVista: 70.7, ChartQA: 88.8, DocVQA: 94.4, MGSM: 90.6 (multilingual), LiveCodeBench: 32.8 | Extreme long-context workloads (multi-document summarization, reasoning over vast codebases, long user-history personalization), native image+text understanding (charts, documents, captioning), multilingual chat across 12 languages, and single-GPU local/commercial deployment.

— | Speed-critical inference, real-time applications, rapid response scenarios

Inception's flagship 260K-context diffusion LLM (dLLM), generating and refining text in parallel at over 1,100 tps for real-time agents, code synthesis, and structured workflows accessible with Zero Data Retention on ARMES.

Claw-Eval General: 62.3 — strong everyday performance with full multimodal coverage | Everyday tasks at high throughput, native multimodal Q&A (image/audio/video), cost-efficient see-hear-act workflow

— | Previous generation coding and productivity

SWE-bench: 80.2% — rivals Claude Opus and GPT-5.2 | Coding, debugging, refactoring, full-stack development, front-end components, test coverage, technical implementation.

PinchBench: 86.2% (5th overall, within 1.2pts of Opus 4.6), SWE-Pro: 56.22%, GDPval-AA: 1495 Elo (highest open-source), MLE Bench Lite: 66.6%, Terminal Bench 2: 57.0% | Agentic coding, full-project delivery, complex engineering systems, autonomous debugging, production incidents, office productivity.

SWE-Bench Pro: 59.0% (#3 public leaderboard; beats GPT-5.5 58.6% & Gemini 3.1 Pro 54.2%, trails Opus 4.7/4.8), SWE-Bench Verified: 85.0%, Terminal-Bench 2.1: 66.0%, MCP Atlas: 74.2%, BrowseComp: 83.5 (beats Opus 4.7's 79.3), OSWorld-Verified: 70.06%, SWE-fficiency: 34.8%, KernelBench Hard: 28.8%, SVG-Bench: > Opus 4.7, OmniDocBench: > Gemini 3.1 Pro | Long-horizon agentic coding, full-project delivery, autonomous engineering, multimodal document/chart/video understanding, computer-use (desktop operation), and million-token long-context reasoning at frontier-class quality for ~5–10% of proprietary-model cost.

Ministral-14b-2512 is a high-performance foundation model engineered by Mistral AI, accessible with Zero Data Retention on ARMES.

GPQA Diamond: 79.23% (82.70% w/ tools), HMMT Feb 2025: 93.67% (94.73% w/ tools), AIME 2025: 90.21%, MMLU-Pro: 83.73%, LiveCodeBench v5: 81.19%, SWE-Bench Verified: 60.47%, SWE-Bench Multilingual: 45.78%, RULER @1M: 91.75% (vs GPT-OSS-120B's 22.30), RULER @256K: 96.30%, AA Intelligence Index: 36 | Agentic reasoning, tool calling, long-context workflows, instruction following, multi-agent orchestration.

SWE-Bench Verified: 51.56% (BF16) / 52.80% (NVFP4), PinchBench: 85.37%, GPQA Diamond: 75.44%, MMLU Pro: 81.94%, Terminal-Bench 2.1: 24.58%, HLE: 11.72%, IFBench (loose): 71.88%, BrowseComp: 36.97%, AA Intelligence Index: 23.6, AA Coding Index: 26.8, AA Agentic Index: 13.8, AA Non-Hallucination Rate: 62.4% | NVIDIA's highest-efficiency model, purpose-built for the execution layer of always-on agents (released Aug 11, 2026).

General-purpose tasks, balanced reasoning and cost

Ultra-lightweight tasks, highest throughput

GPQA Diamond: 82.4%, SWE-bench: 73.1%, AIME: 93.1% | General chat, simple Q&A, translation, notes retrieval, simple scripts, moderate analysis, standard tasks.

Strong on long-document and routine coding | Cost-effective high-volume tasks, long-document processing, routine coding, local deployment on consumer hardware.

Terminal-Bench 2.1: 82.7%, CyberGym: 76.7%, Toolathlon-Verified: 70.3%, DSBench-FullStack: 68.7%, DSBench-Hard: 59.6%, DeepSWE: 54.4%, NL2Repo: 54.2%, Agents' Last Exam: 25.2%, AutomationBench Public: 25.1%, Artificial Analysis Intelligence Index: 50 | The official GA release of V4 Flash, superseding the April preview (released Jul 31, 2026).