AI Model Pricing Guide

OpenAI's flagship, announced Sep 3 and live on the API Sep 4. $10/$50 per 1M — 5x GPT-6.1 Sol's price — with $1 cached input, $12.50 cache writes, and a long-context tier above 272K input (2x input/cache, 1.5x output). Batch/Flex 50% off; Fast mode 2x. Saturates ARC-AGI-3 (99.9%) and FrontierMath Tier 4 (98%); GPQA 96.0%. AA Intelligence Index v4.3.2: 53 max (Opus 5.5 leads at 58), but it still wins where GPT-6.1 Sol breaks down: Terminal-Bench-Science 68.1% vs Sol's 57.0% and AutomationBench 41.4% vs 36.1%. DevDay Sep 29: Ultrafast tier live in the API and ChatGPT Work/Codex (Pro 500, Enterprise) — up to 6x faster in the API, 8x in Codex at premium pricing. And in the saga of the week: OpenAI cancelled the GPT-6.1 Astra upgrade over safety regressions rather than ship it. Escalate here when 6.1 Sol measurably loses — science, hardest multi-app automations, retry after a Sol failure.

OpenAI's DevDay model (Sep 29), a major in-place upgrade to GPT-6 Sol. Same $2/$10 per 1M as GPT-6 Sol but cached input halves to $0.10; batch $1/$5. Near matches GPT-6 Astra ($10/$50) on agentic coding, computer use and professional work at 8–23% of Astra's cost per task — matches Astra's best DeepSWE v1.1 result (75.2% at high effort). AA Intelligence Index (max): 52 vs GPT-6 Sol's 48 and Astra's 53 — within 3.5 points of Astra on 8 of 10 AA charts. Terminal-Bench 4.0: 56.1% (GPT-6 Sol: 43.9%); OSWorld 2.0: 71.4% vs 64.4%; GDP.pdf beats Opus 5.5's fallbacks at less than half the cost per task. Weak spot: science terminal work — 11.1 points behind Astra. System card: lower failure rates than GPT-6 Sol on transparency and respecting restrictions. Oct 8: the Ultrafast tier went GA for all API users at $12/$60 — a flat 6x on every line (cached input $0.60, cache writes $15; long-context >272K $24/$90) — same weights, same answers. In Codex it carries the up-to-8x badge (the 6x is the API rate-card math). Live in Work and Codex for all paid plans (not yet in Chat); API ID gpt-6.1-sol; on AWS Bedrock same day.

OpenAI's previous #1. Still elite for complex, multi-step problems.

OpenAI's strongest model, now GA. SOTA on Agents' Last Exam (53.6, +13 over Fable 5), BrowseComp (92.2%), Terminal-Bench 2.1 (88.8%). Three tiers: Sol (flagship), Terra (balanced, $2/$12), Luna (fast, $0.20/$1.20). New Fast mode: 2.5x faster at $10/$60 (API). Multi-agent 'ultra' mode for demanding tasks. Aug 21 promo repricing: $4/$20 through Nov 21, then $5/$30 standard. Superseded Sep 3 by GPT-6 Astra at 2.5x the price — Sol remains the value pick for frontier agentic work. Sep 22: GPT-6 Sol launched at $2/$10 — half this promo price — trained with Astra methods and scoring higher on AA v4.3.2 (48 vs 47).

90.1% BrowseComp, 52.4% FrontierMath Tier 1-3. 1.05M context window — read entire codebases and research libraries. The ceiling for what AI can do right now.

Microsoft's entry into the decision-model category that Liquid d1 opened (Oct 9, available in Microsoft Foundry; OpenRouter support planned). Not an LLM: it picks from predefined choices and assigns probability scores — routing, classification, prioritization, verification and workflow control, so software can branch without generating tokens. Pricing: $0.042 per 1M input tokens, output free — Microsoft's internal estimates put classifying 1M texts at ~$11 vs GPT-6 Sol's ~$2,434. Built on Qwen3.5-9B via single-pass decision-scoring post-training, with plans to rebase on MAI and OpenAI models. Claims: highest accuracy across a 36-benchmark, ~150K-question suite kept blind from training; 4.5x faster than the runner-up Quyet-1.0-Large, 35x faster than GPT-6 Sol. Internal users: Xbox Research categorized 10K+ gaming feedback items at GPT-6-Sol-comparable quality, 14x faster and 200x cheaper; Copilot's response-quality team found it competitive with GPT-5.6 Luna. All vendor-measured — launch-week claims, independent verification pending.

Anthropic's flagship-workhorse (Sep 22), first of the Claude 5.5 family. $4/$20 per 1M — 20% under Opus 5 — with cache reads cut 60% to $0.20 (cache writes $5) and 30%+ faster output. Fast mode: $8/$40 at up to 2.5x speed. Batch: $2/$10. Anthropic's tests put total run cost 40% below Opus 5, and five-hour limits rose on Pro/Max/Team/Enterprise. AA Intelligence Index v4.3.2 (max): 58 — the highest score AA has measured, ahead of Sonnet 5.5 (56), Fable 5.1 and GPT-6 Astra (53). Leads six of ten Index evals: HLE 61.4%, SciCode 66.9%, AA-Briefcase 1822 Elo, GDPval-AA v2.1 1846. Best automated-alignment scores Anthropic has measured; bio/cyber capability comparable to Mythos 5.1, so it ships with Fable-5.1-class safeguards. Still clearly stronger than Sonnet 5.5 at complex open-ended work — Anthropic's own testing — but Sonnet 5.5 wins Terminal-Bench 4.0 (70.6% vs 66.4%).

Anthropic's second 5.5-family model (Sep 28), six days after Opus 5.5 — and the sharpest price-performance move of the month. $2/$10 per 1M — identical pricing to Sonnet 5 — but it needs far fewer tokens per task (up to 30% cheaper) and outputs 30%+ faster (~134 tok/s on AA, its fastest Sonnet). Oct 8 update: cache reads halved to $0.10/1M (from $0.20), which Anthropic pegs at ~20% cheaper on most agentic work where cache hits dominate. AA Intelligence Index: 56 — #2 overall behind only Opus 5.5's 58, ahead of Fable 5.1 and GPT-6 Astra (53) — at a quarter of Fable's price. Terminal-Bench 4.0: 70.6% at max effort (43.0% at high — the API default) vs Sonnet 5's 10.3% and above Opus 5.5's 66.4%. First Sonnet to beat Pokémon Red from screenshots alone. Two points below Opus 5.5 on GDPval-AA. First Sonnet with cyber safeguards matching Opus-class capability + reasoning-extraction classifiers: risky security requests fall back to Sonnet 5. 1M context, 128K output (300K on Batch beta); API ID claude-sonnet-5-5. The catch: verbosity — AA measured the highest output tokens per task it has seen, so the halved cache reads are exactly where the value lands.

Anthropic's new frontier (Sep 1). $10/$50 unchanged but cache reads cut 75% to $0.25/1M — Anthropic's August usage data shows ~25% total savings on typical workloads, up to ~45% for agentic work where cache hits dominate. Terminal-Bench-Science 52.6% (vs 24.7% on Fable 5), Terminal-Bench 4.0 55.8%, AutomationBench 31.4%, GDPval-AA v2 1853, CursorBench 73.4%. AA Intelligence Index (max, fallback): 66 — #1, three clear of Opus 5. Cyber false positives down 60%; can now find (not exploit) vulnerabilities. Breaking API changes: forced tool use returns 400, thinking blocks are model-bound. API ID: claude-fable-5-1. Sep 22 update: Opus 5.5 passed it on the AA Index (58 vs 53 max on v4.3.2) at $4/$20 with cheaper cache reads — Fable 5.1 remains the restricted frontier tier.

Public version of Mythos. $10/$50 — double Opus 4.8. 1M context, 128K output, autonomous self-verification. Includes safety classifiers that can refuse requests (refusals are not billed; fallback credits refund prompt-cache cost on retry). Suspended by US government Jun 12, restored globally Jul 1. Superseded Sep 1 by Fable 5.1 — same $10/$50 with cache reads at $0.25/1M.

xAI's most capable model (Sep 21), shipped after four missed release windows. Same $2/$6 pricing as Grok 4.6 with $0.50 cached input. AA Intelligence Index: 46 at xhigh (+2 over 4.6), placing xAI in the top-4 labs; AA-Briefcase 1657 Elo (+111) — just behind Opus 5 and Fable 5.1; GDPval-AA 1695 (+90). CursorBench 4.0 frontier price-performance; DeepSWE v1.1 71.0% (high effort); Terminal-Bench 4.0 38.0%; EEBench, Harvey Legal, HealthBench Professional gains. Works longer and self-checks with xAI's best-calibrated safeguards. The catch: 81k output tokens per task at xhigh — double Grok 4.6's 38k and ~3x GPT-6 Astra's 27k, so the same per-token bill can cost more per task. Knowledge cutoff May 2026. Live in Cursor, Grok Build, the Grok API and model routers.

xAI's new flagship (Aug 12), released with Cursor. $2/$6 per 1M, $0.50 cached input. 1753 ELO claim — overtakes Kimi K3. Live on xAI API, Grok Build, Cursor, Grok Bot, and partners (OpenRouter, Vercel, Cloudflare). Fast variant available at twice the price. 2x included usage in Cursor/Grok Build for the first week. Sep 21: Grok 4.7 launched at the same $2/$6 — AA Index 46 (+2), AA-Briefcase 1657 Elo, but roughly double the output tokens per task.

xAI's recommended Grok 4 model after retiring old variants. Beats Grok 4.1 on coding, agents, and reasoning. This is the migration target for retiring models.

Meta's fourth Muse Spark in five months (Sep 2). $1.25/$4.25 unchanged ($0.15 cached input). AA Intelligence Index: 61 at xhigh (up 4 from 1.2), 62 at max — limited partner preview — behind only Fable 5.1 (66) and Opus 5 (63). #1 on Tau3-Bench Banking. $0.55 per Index task vs $0.94–0.95 for GPT-5.6 Sol and Grok 4.6 — the cheapest of any model at 59+. Meta teases open weights and larger Muse models around Q4.

Google announced its next frontier model on Sep 30 and then largely declined to ship it. Announced intro pricing: $2/$10 per 1M with cached input at 95% off and a 1M-token output limit (up from 64K). Published results: DeepSWE v1.1 77.9% (Opus 5.5: 74.2%), AutomationBench 51.3%, LVBench 91.7%, CWE-bench v1 68% (tied first), #1 on the Vals AI model index. Trained for long-horizon work: Google says Argon agents migrated 800K+ lines of the Fuchsia Zircon kernel to Rust and freed 300+ TiB of datacentre memory. The catch: access is phased through the Fairwind Program (vetted cyber defenders, 650+ partners) and US-government pre-release testing — developers, enterprises and consumers wait, starting with paid API customers and Google AI Ultra subscribers when the guardrails are ready. No date given; not on any public API yet.

Google's best reasoning/coding Flash yet (Sep 2) — 3.5, 3.6, 3.7, 3.8 since May. Same $0.75/$3.75 intro as 3.7 Flash through Dec 31, then $1.50/$7.50 from Jan 1, 2027. AA Intelligence Index 59 (+3 over 3.7) — cheapest model at its intelligence level ($0.58/task). ~300 tok/s, the fastest output speed Artificial Analysis has measured. Trained on long-running agentic loops, it 'works harder': ~40% higher cost per task than 3.7 (30% more output tokens) — drop to low effort for efficiency-first routing. Gemini 3.8 Flash Cyber for trusted defenders via the Fairwind Program: 2.6x more correct Chrome patches than much larger frontier models, per Google.

Google's new flagship, launched July 17. 2-million-token context window — double anything at the frontier. New Deep Think extended reasoning mode (gated to $250/mo Ultra tier). Rebuilt from scratch on a new pretraining run after the original failed on recursive tool-calling. $1.25/$10 — 4x cheaper input than GPT-5.6 Sol.

Reflection AI's first open-weight model (Oct 5), red-teaming now — weights, tech report and API pricing land later this month, so there is no price to show yet. 501B-param MoE with 23B active, pretrained on 23.8T tokens, then one of the largest open-lab RL runs: 100M+ rollouts on 10.5K NVIDIA GB300s over 4 weeks (~1.3B sandboxes, 110K concurrent rollouts). Scores: SWEBench Verified 80.9, Terminal-Bench v2.1 80.1, SWE-Bench Pro v1 65.5, DeepSWE v1.1 44.4 — GLM-5.2-class with 3-4x less inference compute; Kimi K3 still leads raw capability. Reasoning-effort control trades tokens for depth. From Mislav Balunović & Wei Chen (ex-DeepMind, AlphaZero lineage); $25B valuation, 250MW Korean sovereign build, SpaceX Colossus-2 lease.

Mistral's biggest model yet (Oct 6 public preview, 'le Chonk'): 1.05T-param MoE with 52B active per Mistral's current docs (launch posts said 49B; the model 'continues to improve rapidly as we refine it') plus a 1.6B vision encoder, natively multimodal (image+text in), trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's own European datacentres — the first model out of its €3B round. API: $0.68/$2.09 per 1M with $0.07 cached input during preview; docs show struck-through list pricing of $1.36/$4.18 (cache $0.14) with no end date for the discount — budget on list. Mistral claims SOTA among open models on cyber (82% on AA's find-and-patch test — the highest of any model, where Opus 5.5 and GPT-6 Astra score near zero by refusing — plus 93% on Cybench), finance and law, SOTA open-weight on SciCode-Verified, and beats closed frontier models on visual grounding (Dense 200 42% vs Astra's 41%). AA Intelligence Index v4.3.2 preview: 38.4; Mistral's own brief ranks it top-five globally on the AA Cyber Index and leads open-weight models developed outside China 'by a wide margin'. Coding: DeepSWE v1.1 61.7%, Terminal-Bench 4.0 28.3%, Coding Agent Index 49.8% (ahead of DeepSeek V4 Pro and Qwen 3.8-Max; Kimi K3 and GLM-5.3 still lead), second of five in a blind Surge AI human eval behind Opus 5. AutomationBench 59.9%, AA-Briefcase 1393 Elo. The RL run is still in flight and 'shows no sign of saturation' — numbers will move. Open weights + custom licence drop Oct 27 after a red-team window with cyber partners and state authorities (reduced-moderation version). Vals AI ranks it lower than the hype (#32 of 44 on its index) — the launch cards lean on verticals, not the aggregate.

Liquid AI's first 'decision model' (Oct 5, general availability after last week's experimental drop). Not an LLM: one forward pass reads text + images and returns probabilities for your questions — no generated tokens, 200-300ms per decision. Billed on input only: $0.04/1M, output $0.00; a 1024x1024 image counts ~1,536 tokens. On six real apps (ticket filtering, code search, web agents, visual inspection) it matches or beats GPT-6.1 Sol on four at 19-200x lower cost, answering significantly faster. Three question types: yes/no, choice, score. API model ID d1; also on OpenRouter and Vercel (text-only there so far). Open weights promised for upcoming models.

StepFun's flagship agentic model (launched ~Sep 18 on StepFun's own API at the same $1/$2.70; hit OpenRouter Oct 8 as a router). 600B-param sparse MoE with 27B active, 1M-token context, text+image+video input (max output 64K on StepFun's docs; 128K on OpenRouter; reasoning + tool calling). StepFun's own pitch: 'the Pareto frontier marks the best trade-offs between intelligence and cost — progress begins when that boundary shifts outward.' AA Intelligence Index v4.3.2: 44 — #12, above Kimi K3 and GLM-5.3, at ~$1.03 per Index task vs Sonnet 5.5's $1.08–$7.60 by effort. Vendor numbers: DeepSWE v1.1 67.7% (vs Kimi K3's 67.5), Terminal-Bench v2.1 85.0%, GPQA Diamond 93.5%, BrowseComp 88.7%, FrontierFinance 66.4% — beating GPT-6 Astra's 55.0; a weaker Terminal-Bench v4 of 33.3% vs GLM-5.3's 41.9 shows the aggregate sits below the highlights. Very verbose: 160M output tokens across the AA run, roughly double the median. The stand-out demo: 24 hours unsupervised on an H100 optimizing an MLA GPU kernel to 508 TFLOPS (vs Opus 5's 493). Cache hits bill at ~10% of input ($0.10) — effective blended rate ~$0.54/1M. Open weights land Oct 15; MIT-style licence on the open-source release.

Xiaomi's new flagship (Sep 22), released and open-sourced together: 1.02T-param MoE (42B active) with hybrid attention and multi-token prediction, trained via massively scaled RL on the recursive-self-improvement path. AA Intelligence Index v4.3.2: 46.32 (max) — the strongest open-weight model measured, ahead of GLM-5.3 and Kimi K3 (~44) and past Grok 4.7's 46 on the same revision. Native omni-modal: text, image, video and audio input; 1M context, 128K output. API: $0.435/$0.87 per 1M with cache hits at $0.0036 — flat vs V2.5, which Xiaomi pegs at 1/20 to 1/60 of overseas pricing at equal intelligence. MIT weights, tech report, Distill-Qwen-9B and RL training resources on Hugging Face; an ultraspeed variant serves the same model faster.

DeepSeek's new flagship (Sep 10, 2026) — first model on the Causal Encoder-Decoder (CED) architecture: 552B-param backbone that activates 8B per token on prefill and 16B on decode. Native image input, 1M context, 384K max output. MIT-licensed weights on Hugging Face (~510GB FP8). API: $0.15/$0.60 off-peak, $0.30/$1.20 peak, cache hits $0.003/$0.006 — a 50x gap between cache-hit and cache-miss input. DeepSeek-run benchmarks: DeepSWE v1.1 74.2 (V4 Pro: 62.7), Terminal-Bench 2.1 90.6, CyberGym 88.1. Independent AA Intelligence Index v4.3 (max): 39.5 — above V4 Pro 0813 (36.3) at a third of the peak price. Global KV cache down to 890 bytes/token (~1/4 of V4 Flash). Retires V4-Flash; from Sep 14 all deepseek-v4-pro requests route to V4.1-Flash at Flash rates until a V4.1-Pro ships.

Z.ai (formerly Zhipu AI) calls GLM-5.3 the most capable open-weights model for coding. 743B-param model built by scaling post-training on the GLM-5.2 base. Weights are now public on Hugging Face (MIT-style GLM-5.3 licence, Aug 27) — 51 quantizations available. Live via GLM Coding Plan, ZCode and the API. AA Intelligence Index v4.3 (max): 44.9 — the strongest open-weight model measured. GLM-5.3-Flash (41.9) is the cheap sibling at $0.15/$0.50 with 1M ctx after its Sep 9 promo ended. 'Dramatic improvement over GLM-5.2 with fewer output tokens.' GLM-5.2 API was ~$1.40/$4.40 — roughly a tenth of US frontier per-token rates. Sep 22: Xiaomi's MiMo-V2.6-Pro (46.3 on AA v4.3.2) takes the open-weight #1 spot; GLM-5.3's 44.9 (v4.3) is now the strongest Z.ai model.

Alibaba's first omni-modal agent model (Sep 18). Accepts text, images, audio and video natively — up to 3 hours of audio, 2-hour video files — and returns text, with thinking on by default, function calling and web search. Built on the open-weight Qwen3.8-Flash-Next architecture but served API-only (QwenCloud, Model Studio); no downloadable weights. QwenCloud pricing: $0.15/$0.47 per 1M, implicit cache hits $0.016 — audio input under $0.01/hour, ~$0.20 per minute of 720p video. +25% average across 29 evals over Qwen3.5-Omni-Plus; WildClawBench-MM 34.5 to 71.0, UniClawBench 69.6 (Gemini 3.8 Flash: 69.0), though Gemini still leads AgenticVBench (45.0 vs 36.8). SWE-bench Pro 63.3, LiveCodeBench v6 92.6. Qwen-MM-Plugins (Apache-2.0) add audio/video handling to Claude Code, Gemini CLI and Qwen Code; a Realtime variant handles live audio.

Alibaba's most capable model. 2.4T-param MoE with 95B active. $2/$6 per 1M — flat across the entire 1M-token context window (no long-prompt surcharge). Cached input at $0.25/1M. Claims 'second only to Fable 5'. AA Intelligence Index v4.1.1: 58 pts (#8 globally, between GPT-5.6 Sol high and GPT-5.6 Terra max). Multimodal (text + visual). Open weights promised within days of GA. OpenAI and Anthropic API compatible.

Tencent's next-gen Hunyuan, launched Aug 28 2026. 770B total / 49B active MoE with a context window exceeding 1M tokens. Apache 2.0 weights on Hugging Face. API: $0.834/$2.501 per 1M ($0.042 cache hits) via Tencent Cloud TokenHub and OpenRouter. Free for 2 weeks on WorkBuddy & CodeBuddy; Hy3 free access extended to Sep 30. Targets coding, office work, game dev, and scientific research. More Hy4-series models coming soon.

Moonshot AI's flagship. 2.8-trillion-parameter MoE — world's largest open-weight model. Scores 57 on the Artificial Analysis Intelligence Index — third overall (behind Fable 5 ~60, GPT-5.6 Sol ~59), first open-weight model ever in the top 3. First on LMArena Frontend Code Arena (1,679 Elo). Native multimodal (text + images), 1M context. $3/$15 — averages $0.94 per AA Index task vs Sol $1.04, Opus 4.8 $1.80. Full weights public Jul 27 (1.56TB). Shook US chip stocks on release.