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AI Coding Live News

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Artificial AnalysisArtificialAnlys·
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Artificial Analysis: Tencent Hy4 Scores 35 on Intelligence Index v5, Ranked No.2 Among China Labs, 31% on FrontierCode 2.0

Independent evaluator Artificial Analysis published Tencent Hunyuan Hy4 results on the Intelligence Index v5 on November 5 (08:28 UTC): a composite score of 35, tying Grok 4.7 (xhigh) and one point behind Claude Haiku 5.5 (max). Among China-based labs it ranks second only to Kimi K3.1 Pro (46). On agentic coding it scores 31% on FrontierCode 2.0 and 37% on Terminal-Bench 4.0; cost per Intelligence Index task is about $0.78, roughly one-tenth of same-score Grok 4.7.

Artificial Analysis: Tencent Hy4 Scores 35 on Intelligence Index v5, Ranked No.2 Among China Labs, 31% on FrontierCode 2.0

Key Takeaways

  • Intelligence Index v5 composite 35: ties Grok 4.7 (xhigh), one point behind Haiku 5.5 (max, 36); No.2 China-lab model behind Kimi K3.1 Pro (46)
  • FrontierCode 2.0 at 31% (leads China labs; DeepSeek V4.1 Flash 1017 max 28%, Kimi K3.1 Pro 25%); Terminal-Bench 4.0 at 37%
  • AA-Omniscience v1.1 accuracy 50% but hallucination rate 89% (Kimi K3.1 Pro hallucination 37%); HLE-Diamond 33%
  • About $0.78 per Intelligence Index task: ~10x cheaper than same-score Grok 4.7, ~13x cheaper than Qwen3.8 Max (34), nearly 2x Haiku 5.5
  • Pricing about $0.834/$2.501 per 1M input/output tokens, cache reads $0.042; 1M context; text and image input
Read full story
Linked Models & Agents:
⚡ Tencent Hy4⚡ Kimi K3.1 Pro⚡ Grok 4.7⚡ Claude Haiku 5.5⚡ DeepSeek-V4.1-Flash🌐 Tencent🌐 Artificial Analysis🌐 Tencent Hunyuan
#Benchmark#Hy4#Intelligence Index#FrontierCode#Tencent#Artificial Analysis
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Artificial AnalysisArtificialAnlys·
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Artificial Analysis Makes Time per Task Its Headline Performance Metric: GPT-6 Astra Finishes a Max-Effort Task in About 17 Minutes, Ahead of Opus 5.5 and Gemini 4 Carbon

Independent benchmarker Artificial Analysis announced on X on November 4 (17:34 UTC) a new version of its API performance benchmarking: the headline metric moves from output speed to Time per Task, the time to finish one Intelligence Index task (output tokens per task divided by end-to-end output speed). It covers about 1,000 serverless endpoints, measures speed from sending the request to the final token, and adds multi-turn agentic coding and knowledge-work workloads. Per its data, GPT-6 Astra at max effort runs at 51 tokens/s yet finishes a task in about 17 minutes, against about 26 for Claude Opus 5.5 (max) and about 29 for Gemini 4 Carbon (high).

Key Takeaways

  • Time per Task equals output tokens per Intelligence Index task divided by end-to-end output speed, so a model that is fast per token but verbose can finish later than a slower model that uses fewer tokens.
  • End-to-end output speed runs from sending the request to the final token, including the wait before the first token (prefill and hidden reasoning); output tokens now come from each provider's API-reported counts instead of the o200k_base tokenizer.
  • GPT-6 Astra (max) runs at 51 tokens/s with about 50k output tokens per task and takes about 17 minutes; Claude Opus 5.5 (max, 111 tokens/s) takes about 26 minutes and Gemini 4 Carbon (high, 71 tokens/s) about 29, using roughly 120k to 170k tokens per task.
  • At each lab's API default effort (medium for all three), GPT-6 Astra, GPT-6.1 Sol and Opus 5.5 cut Time per Task by about 3 to 5x and Cost per Task by about 2 to 4x, scoring about 5 to 11 points lower; Opus 5.5 drops from about 26 minutes and $10.04 to about 6.8 minutes and $2.45.
  • The official methodology page lists this as version 3.0.0 (October 30), with runs now every 6 hours and a new endpoint error rate metric.
Read full story
Linked Models & Agents:
⚡ GPT-6 Astra⚡ GPT-6.1 Sol⚡ Claude Opus 5.5⚡ Gemini 4 Carbon🌐 Artificial Analysis🌐 OpenAI🌐 Anthropic🌐 Google
#Artificial Analysis#Time per Task#Benchmark#API performance#Agentic#Latency
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Artificial Analysis@ArtificialAnlys·
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Artificial Analysis Upgrades Its Six Capability Indexes to v2.0, Weighting Non-Hallucination and Agentic Tool Use; Claude Opus 5.5 Leads All Six

Independent evaluator Artificial Analysis announced on X on November 4 that its six Capability Indexes (Finance & Accounting, Strategy & Ops, Legal, Healthcare & Medical, Engineering, Economics) are now v2.0, aligned with Intelligence Index v5.0 evaluations and weighting Non-Hallucination and Agentic Tool Use more heavily. Across the indexes, HLE becomes HLE-Diamond, AA-Omniscience moves to v1.1 and GDPval-AA is removed; Engineering gains a new Science capability. Per Artificial Analysis, Claude Opus 5.5 (max) leads all six, with GPT-6 Astra (max) second in Finance & Accounting, Legal and Engineering. Among open-weights models, Kimi K3.1 Pro (weights not yet released) leads all six at #5 to #8.

Artificial Analysis Upgrades Its Six Capability Indexes to v2.0, Weighting Non-Hallucination and Agentic Tool Use; Claude Opus 5.5 Leads All Six

Key Takeaways

  • The six indexes map O*NET occupation tasks to representative benchmarks, weighted by how often each capability appears in that occupation (methodology page)
  • Common changes: HLE becomes HLE-Diamond, AA-Omniscience moves to v1.1, and GDPval-AA is dropped from Agentic Knowledge Work (now AA-Briefcase v1.1)
  • Non-Hallucination weight rises to 15% in Finance & Accounting and Legal; Agentic Tool Use (AutomationBench-AA) is 35% of Strategy & Ops, its largest component
  • Engineering adds Science (20%, CritPt v2 Hard Subset plus Terminal-Bench-Science 0.1) and FrontierCode 2.0 under Agentic Coding; Economics swaps AA-LCR for GDP.pdf
  • Results per Artificial Analysis: Claude Opus 5.5 (max) is #1 in all six; among open weights, Kimi K3.1 Pro leads all six, MiniMax-M3.1-Flash leads Strategy & Ops (#10) and DeepSeek V4.1 Flash 1017 (max) leads Healthcare & Medical (#12)
Read full story
Linked Models & Agents:
⚡ Claude Opus 5.5⚡ GPT-6 Astra⚡ Kimi K3.1 Pro⚡ MiniMax-M3.1-Flash⚡ DeepSeek-V4.1-Flash🌐 Artificial Analysis🌐 Anthropic🌐 OpenAI🌐 Moonshot🌐 MiniMax🌐 DeepSeek
#Artificial Analysis#Capability Indexes#v2.0#Benchmark#Non-Hallucination#Agentic Tool Use#Claude Opus 5.5
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Artificial AnalysisArtificialAnlys·
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Artificial Analysis releases AA-Omniscience v1.1: 3,000 refreshed questions and a calibrated 300-question public set, with Gemini 4 Carbon leading at a Net Score of 54

On October 31, independent evaluator Artificial Analysis released AA-Omniscience v1.1, its knowledge and hallucination benchmark and part of Intelligence Index v5. The question set shrinks from 6,000 to 3,000, dropping previously public and saturated questions, fixing errors and ambiguities, and adding new ones, and a new 300-question public set on Hugging Face is calibrated to the private set's difficulty, with 0.99 Pearson and Spearman correlation on Net Score. Gemini 4 Carbon (high) leads with a Net Score of 54, ahead of Claude Opus 5.5 at 48 and Claude Fable 5.1 at 46; Fable 5.1 has the highest accuracy (68%) and Gemini 4 Argon the lowest hallucination rate (4%). Models score about 1.9 points lower than on v1 on average, and MiniMax M3.1 Flash drops 22 points. All figures are as reported by Artificial Analysis.

Artificial Analysis releases AA-Omniscience v1.1: 3,000 refreshed questions and a calibrated 300-question public set, with Gemini 4 Carbon leading at a Net Score of 54

Key Takeaways

  • The question set shrinks from 6,000 to 3,000 across 6 domains and 42 topics: 600 each for science/engineering/math and software engineering, 450 each for finance, health, humanities/social sciences and law
  • A new 300-question public set is on Hugging Face, with 0.99 correlation to private-set Net Score and a median difference of 0.0 points
  • Net Score ranking: Gemini 4 Carbon (high) 54, Claude Opus 5.5 48, Claude Fable 5.1 46; the best open-weights model, Muse Spark Open, trails Gemini 4 Carbon by 28 points
  • Claude Fable 5.1 has the highest accuracy (68%), followed by Claude Opus 5.5 (66%); Gemini 4 Argon has the lowest hallucination rate (4%)
  • Rank order keeps a 0.98 Spearman correlation with v1, but scores are 1.9 points lower on average; MiniMax M3.1 Flash falls from 16 to -6 and MiMo V2.6 Pro from 8 to -5 (vendor-reported)
Read full story
Linked Models & Agents:
⚡ Gemini 4 Carbon⚡ Claude Opus 5.5⚡ Claude Fable 5.1⚡ Gemini 4 Argon⚡ Muse Spark Open⚡ MiniMax-M3.1-Flash⚡ MiMo V2.6 Pro🌐 Artificial Analysis🌐 Google🌐 Anthropic
#AA-Omniscience#Hallucination#Benchmark#Artificial Analysis#Intelligence Index v5
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Google DeepMind@GoogleDeepMind·
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Google releases Gemini 4 Carbon two weeks after Argon: 59 and first place on the Artificial Analysis Intelligence Index v5, state-of-the-art cybersecurity and long-horizon coding, with early access for cyber defenders in the Fairwind program

Google DeepMind announced Gemini 4 Carbon on Oct 28, calling it its strongest model, just two weeks after Gemini 4 Argon. The official blog says it ranks first on v5.0 of the Artificial Analysis Intelligence Index at 59 and leads three cybersecurity evaluations: 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation and 71.0% on CWE-bench for patching; it also gains 89 points to rank fourth on WebDev Arena at 1769. Access starts this week for a subset of cyber defenders in the Fairwind program, with developers, businesses and consumers to follow. Introductory pricing matches Argon at $2 per million input tokens and $10 per million output tokens, rising to $4 and $20 afterwards.

Google releases Gemini 4 Carbon two weeks after Argon: 59 and first place on the Artificial Analysis Intelligence Index v5, state-of-the-art cybersecurity and long-horizon coding, with early access for cyber defenders in the Fairwind program

Key Takeaways

  • Positioning: Google calls it its strongest model, aimed at cybersecurity and long-horizon agentic work; internal teams saw it run for days to root-cause and patch subtle bugs across large codebases and low-level systems
  • Official scores (evaluated without guardrails): 88.2% on SEC-bench Pro, 54.8% on ExploitGym and 71.0% on Collinear's CWE-bench; 59 and first place on the Artificial Analysis Intelligence Index v5.0; +89 to fourth place on WebDev Arena at 1769
  • Safety and alignment: successful cheating on Terminal-Bench 4.0 fell to 0.0% with a 0.4% attempt rate, high-severity misalignment alerts in internal live coding dropped by almost 80%, unsupported claims are about half of Argon's, and the Gray Swan indirect prompt injection attack success rate fell from Argon's 0.7% to 0.0%
  • Internal examples: more than 2x Argon's success rate at generating secure code, and the Chrome team reports more than 10x better high-quality patches than another lab's large frontier model
  • Rollout: a subset of cyber defenders in the Fairwind program first, alongside the U.S. government's voluntary pre-release testing; no developer API yet; introductory pricing of $2 / $10 per million tokens with cached input 95% off, then $4 / $20
Read full story
Linked Models & Agents:
⚡ Gemini 4 Carbon⚡ Gemini 4 Argon🌐 Google🌐 Google DeepMind🌐 Artificial Analysis
#Google#Gemini#Gemini 4 Carbon#Cybersecurity#long-horizon agents#frontier model
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Artificial Analysis@ArtificialAnlys·
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Artificial Analysis launches Intelligence Index v5: 60% private test sets, Claude Opus 5.5 leads at 58 and the open-weights gap widens to 12 points

On Oct 28 independent evaluator Artificial Analysis released Intelligence Index v5, its biggest revision since v4 in January: it adds Cognition's FrontierCode 2.0 and Terminal-Bench-Science, swaps Humanity's Last Exam for HLE-Diamond, and now draws 60% of its weight from private or held-out test sets. Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra at 57 and GPT-6.1 Sol at 55; the top open-weights model, Kimi K3.1 Pro (weights not yet released), scores 46, widening the gap from 3 points in v4.3 to 12. It also launched Progress Index v1 for comparing models across years.

Artificial Analysis launches Intelligence Index v5: 60% private test sets, Claude Opus 5.5 leads at 58 and the open-weights gap widens to 12 points

Key Takeaways

  • Overall (vendor-reported by Artificial Analysis): Claude Opus 5.5 (max) 58, GPT-6 Astra (max) 57, GPT-6.1 Sol (max) 55; Anthropic and OpenAI hold the top five, with Gemini 4 Argon (high) next at 50
  • The open-weights gap grows from 3 to 12 points: Kimi K3.1 Pro scores 46, while released-weight leaders DeepSeek V4.1 Flash 1017 and GLM-5.3 (max) score 32, below Claude Haiku 5.5 (max) at 36
  • Cost per task varies more than 13x: among the top 10, GPT-6.1 Terra (max) is cheapest at $0.96 and Claude Fable 5.1 (max) priciest at $13.04; GPT-6.1 Sol trails Opus 5.5 by 4 points at about one eighth the cost ($1.27 vs $10.04)
  • Four categories at 25% each: Knowledge Work, Coding (FrontierCode 2.0 15%, Terminal-Bench 4.0 10%), Science and General; GDPval-AA v2.1, SciCode and AA-LCR were removed
  • Tasks are heavier: median output tokens per task rose from 35k to 59k, and effort matters more, with Opus 5.5 scoring 35 at low effort vs 58 at max for 10x the cost
Read full story
Linked Models & Agents:
⚡ Claude Opus 5.5⚡ GPT-6 Astra⚡ GPT-6.1 Sol⚡ GPT-6.1 Terra⚡ Gemini 4 Argon⚡ Kimi K3.1 Pro⚡ DeepSeek-V4.1-Flash⚡ GLM-5.3🌐 Artificial Analysis🌐 Anthropic🌐 OpenAI🌐 Google🌐 Moonshot🌐 DeepSeek🌐 Zhipu🌐 Cognition
#Artificial Analysis#Intelligence Index#Benchmark#FrontierCode 2.0#Terminal-Bench#HLE-Diamond#open weights
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Ant Ling@AntLingAGI·
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Ant Ling open-sources Ling-3.1-flash-1019: further post-trained 560B/25B-active MoE lifts AA Intelligence Index from 41 to 44, base checkpoint also released

On Oct 27 Ant Ling (inclusionAI) open-sourced Ling-3.1-flash-1019, a further post-trained upgrade of the early-October Ling-3.1-flash: 560B total parameters, 25B active per token, a 1M-token context and an MIT license. Ant says it scores 44 on the Artificial Analysis Intelligence Index v4.3.2 (up from 41), with clear gains on agent tasks such as AA-Briefcase and AutomationBench-AA. It also released Ling-3.1-flash-base, an open foundation checkpoint for further training.

Ant Ling open-sources Ling-3.1-flash-1019: further post-trained 560B/25B-active MoE lifts AA Intelligence Index from 41 to 44, base checkpoint also released

Key Takeaways

  • Scale: 560B total and 25B active parameters, 1M-token context, MIT license, on Hugging Face and ModelScope
  • Artificial Analysis Intelligence Index v4.3.2: 44, up from 41 for Ling-3.1-flash (vendor-reported)
  • AA-Briefcase rises from about 1400 to 1524 Elo and AutomationBench-AA from 62% to 68% (vendor-reported)
  • Science: CritPt from 18% to 26% and SciCode from 54% to 57% (vendor-reported)
  • Architecture: hybrid-linear MoE with 63 KDA layers plus 9 gated MLA layers (7:1), 512 routed experts and 8 active per token
Read full story
Linked Models & Agents:
⚡ Ling-3.1-flash-1019⚡ Ling-3.1-flash-base⚡ Ling-3.1-flash🌐 Ant Group🌐 inclusionAI🌐 Artificial Analysis🌐 Hugging Face🌐 ModelScope🌐 SGLang🌐 vLLM
#MoE#Agents
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ModelScope@ModelScope2022·
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StepFun opens Step 5 Preview weights: 600B/27B sparse MoE agent flagship with 1M context now on Hugging Face and ModelScope

On 2026-10-15 StepFun released open weights for Step 5 Preview as promised: a 600B-total / 27B-active sparse MoE with up to 1M-token context and vision input, aimed at agentic software engineering and knowledge work. Checkpoints are on Hugging Face (FP8, BF16, NVFP4) and ModelScope. Official scores include StepCodeBench 49.0%, FrontierFinance 66.4%, and Artificial Analysis Intelligence Index 44. September coverage was API/index only; this is the first public weight drop.

StepFun opens Step 5 Preview weights: 600B/27B sparse MoE agent flagship with 1M context now on Hugging Face and ModelScope

Key Takeaways

  • Specs: sparse MoE 600B total / 27B active, up to 1M-token context, text+image input (model page)
  • Weights: stepfun-ai/Step-5-Preview plus BF16 and NVFP4 on Hugging Face and ModelScope (HF card)
  • Coding: StepCodeBench avg@4 49.0% (leads the open-weight set StepFun cites); DeepSWE v1.1 67.7% (official table)
  • Finance / index: FrontierFinance 66.4%; Artificial Analysis Intelligence Index 44 (matches earlier AA coverage)
  • Serve via official vLLM / SGLang Docker images and OpenAI-compatible APIs; license is StepFun Model License 1.0 (Apache-2.0-style with commercial conditions)
Read full story
Linked Models & Agents:
⚡ Step 5 Preview🌐 stepfun🌐 modelscope🌐 huggingface
#StepFun#Step 5 Preview#MoE#Agents#1M context#Hugging Face#ModelScope
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AI at Meta@AIatMeta·
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Meta Muse Wonder and Wonder Full frontier agent models (Active Memory, Active Focus); Muse Wonder now in Cursor at 46.1% CursorBench, $4/$20

On 2026-10-12 Meta introduced Muse Wonder and Muse Wonder Full, its most capable models yet for personal and professional agentic work. Official posts highlight Active Memory (carry and retrieve what matters across conversations) and Active Focus (persist through hard tasks, recover from errors, clarify and confirm before consequential actions). Muse Wonder is available today via the Meta Model API and Muse Code; Muse Wonder Full is available today for Muse Code subscribers. Third-party Artificial Analysis Intelligence Index (max reasoning) lists Wonder Full at 56 and Wonder at 52 (Spark 1.3 Max was 48). Update Oct 16: Muse Wonder is now in Cursor with 300k context (1M Max Mode at the same rates), $4/$20 per million tokens, and 46.1% on CursorBench per Cursor.

Meta Muse Wonder and Wonder Full frontier agent models (Active Memory, Active Focus); Muse Wonder now in Cursor at 46.1% CursorBench, $4/$20

Key Takeaways

  • Announced 2026-10-12 via Meta Newsroom and @AIatMeta (official post)
  • Availability (Meta): Muse Wonder on Meta Model API + Muse Code; Muse Wonder Full for Muse Code subscribers only
  • Core mechanisms: Active Memory + Active Focus for long-horizon personal/professional agents
  • Third-party AA Intelligence Index (max): Wonder Full 56, Wonder 52 vs Spark 1.3 Max 48 (changelog)
  • Now in Cursor (2026-10-16): muse-wonder-1.0, 300k / 1M Max Mode at same rates, $4/$20 per M tokens; Cursor reports 46.1% CursorBench at default effort (Cursor docs)
Read full story
Linked Models & Agents:
⚡ Muse Wonder⚡ Muse Wonder Full⚡ Muse Spark 1.3🤖 Muse🤖 Muse Code🤖 Cursor🌐 Meta
#Meta#Muse Wonder#Muse Wonder Full#Agent#Model API#Cursor
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Artificial Analysis@ArtificialAnlys·
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Artificial Analysis: StepFun Step 5 Preview scores 44 on Intelligence Index at ~2.8x lower cost than Kimi K3

@ArtificialAnlys evaluated StepFun Step 5 Preview (600B-total / 27B-active MoE): Artificial Analysis Intelligence Index 44, matching Kimi K3 (max) at about $0.72 per Index task (~2.8x cheaper). Pricing is about $1/$2.70 per 1M input/output tokens with $0.05/M cached input; 1M context; open weights planned for October 15. It still lags peers on agentic evals such as GDPval-AA and Terminal-Bench 4.0.

Artificial Analysis: StepFun Step 5 Preview scores 44 on Intelligence Index at ~2.8x lower cost than Kimi K3

Key Takeaways

  • AA Intelligence Index 44, tied with Kimi K3 (max); ~$0.72/task (~2.8x cheaper).
  • Pricing ~$1/$2.70 per 1M in/out with $0.05/M cached input; 600B/27B MoE; 1M context.
  • Open weights planned Oct 15; still trails peers on GDPval-AA and Terminal-Bench 4.0.
Read full story
Linked Models & Agents:
⚡ Stepfun Step 5 Preview⚡ Kimi K3
#Step 5 Preview#StepFun#Artificial Analysis#Intelligence Index#MoE#Pricing#open weights
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Artificial Analysis@ArtificialAnlys·
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Xiaomi open-weights MiMo-V2.6-Pro: AA Intelligence Index 46, tops open weights on cost frontier

@ArtificialAnlys reports Xiaomi released open-weights MiMo-V2.6-Pro: Artificial Analysis Intelligence Index 46 (up from 26 on V2.5-Pro), now the top open-weights model, landing on the intelligence-vs-cost Pareto frontier at about $0.13 per Index task with unchanged token prices ($0.435/M input with 99% cache-hit discount, $0.87/M output). It is a 1.02T-total / 42B-active MoE.

Xiaomi open-weights MiMo-V2.6-Pro: AA Intelligence Index 46, tops open weights on cost frontier

Key Takeaways

  • Open weights: AA Intelligence Index 46, big jump from V2.5-Pro (26) and tops open-weights.
  • Cost: ~$0.13/task; $0.435/M input (99% cache-hit discount) and $0.87/M output unchanged.
  • Architecture: 1.02T-total / 42B-active MoE; more AA analysis forthcoming.
Read full story
Linked Models & Agents:
⚡ Xiaomi Mimo V2 6 Pro
#MiMo-V2.6-Pro#Xiaomi#Artificial Analysis#open weights#MoE#Intelligence Index#Pricing
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Artificial Analysis@ArtificialAnlys·
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Artificial Analysis: Grok 4.7 + Grok Build hits Coding Agent Index 56 (4th); Intelligence Index 46 puts SpaceXAI in top 4 labs

@ArtificialAnlys evaluated Grok 4.7 (xhigh): Intelligence Index 46 (+2 vs 4.6), putting SpaceXAI in the top 4 labs. With Grok Build it scores 56 on the Coding Agent Index (+9 vs 4.6), 4th among native harnesses behind Claude Fable 5.1, GPT-6 Astra, and Claude Opus 5. Pricing stays $2/$6 per 1M in/out ($0.50 cache hits), 500k context.

Artificial Analysis: Grok 4.7 + Grok Build hits Coding Agent Index 56 (4th); Intelligence Index 46 puts SpaceXAI in top 4 labs

Key Takeaways

  • Coding Agent Index: Grok 4.7 + Grok Build scores 56 (+9), 4th among native harnesses.
  • Intelligence Index 46 (+2) puts SpaceXAI in the top 4 labs; +111 Elo on AA-Briefcase.
  • Same $2/$6 per 1M pricing as 4.6 ($0.50 cache hits), 500k context; gains come with higher token use.
Read full story
Linked Models & Agents:
⚡ Grok 4.7🤖 grok-build🌐 xAI
#Grok 4.7#Artificial Analysis#Coding Agent Index#Intelligence Index#Grok Build#Benchmark#Pricing
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Cerebras@cerebras·
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Cerebras launches Qwen3.8-27B: open dense model scores 34 on AA Index, near GPT-5.6 Luna / DeepSeek V4 Pro tier

Cerebras said Alibaba's open-weight dense Qwen3.8-27B is live at Cerebras speed. It scores 34 on the Artificial Analysis Intelligence Index—comparable to models such as GPT-5.6 Luna, DeepSeek V4 Pro, and Claude Sonnet 4.6. Qwen also confirmed rapid inference on Cerebras.

Key Takeaways

  • Qwen3.8-27B live with Cerebras-speed inference.
  • AA Intelligence Index 34—near Luna / DeepSeek V4 Pro / Sonnet 4.6 tier.
  • Open dense weights; Qwen confirms Cerebras availability.
Read full story
Linked Models & Agents:
⚡ qwen3🌐 alibaba🌐 cerebras
#Qwen#Qwen3.8-27B#Cerebras#open weights#Artificial Analysis#Release
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Artificial Analysis@ArtificialAnlys·
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Artificial Analysis Intelligence Index v4.3: Terminal-Bench 4.0 + AutomationBench-AA

Artificial Analysis (@ArtificialAnlys) shipped Intelligence Index v4.3: Terminal-Bench upgraded 2.1→4.0 (now mini-SWE-agent harness) and τ³-Banking replaced by AutomationBench-AA (657 private Zapier business workflows). Claude Fable 5.1 and GPT-6 Astra tie at 53; OpenAI holds most of the intelligence-vs-cost frontier across Astra reasoning efforts.

Key Takeaways

  • Benchmarks: Index v4.3 upgrades Terminal-Bench to 4.0 and adds AutomationBench-AA (private set).
  • Weighting: private-task/answer evals rise from 40% to 45%; category weights unchanged.
  • Results: Claude Fable 5.1 and GPT-6 Astra tie at 53; open-weights led by GLM-5.3 / Kimi K3 at 44.
Read full story
Linked Models & Agents:
⚡ GPT-6 Astra⚡ Claude Fable 5.1🌐 OpenAI🌐 Anthropic
#Artificial Analysis#Intelligence Index#Terminal-Bench#AutomationBench-AA#GPT-6 Astra#Claude Fable 5.1#Benchmark
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Artificial Analysis@ArtificialAnlys·
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MiniCPM5-2B scores 15 on AA Intelligence Index v4.2 — top open-weights model under 4B

Artificial Analysis (@ArtificialAnlys) scored OpenBMB MiniCPM5-2B (2.6B dense, 131k context, Apache 2.0) at 15 on Intelligence Index v4.2 — highest among open-weights models under 4B total parameters. Strong small-model agentic results (GDPval-AA / τ³-Banking); coding/long-context benches like Terminal-Bench lag larger peers.

Key Takeaways

  • Model: OpenBMB MiniCPM5-2B — 2.6B dense, 131k context, Apache 2.0.
  • Score: AA Intelligence Index v4.2 = 15, highest open-weights under 4B (vs Granite 4.2 3B at 11).
  • Tradeoff: strong small-model agentic benches; weaker on Terminal-Bench / HLE-style coding and knowledge.
Read full story
Linked Models & Agents:
⚡ Openbmb Minicpm5 2b🌐 openbmb
#MiniCPM5-2B#OpenBMB#Artificial Analysis#Intelligence Index#open weights#Benchmark