Entity trail

GPT-5.6 Terra

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-10 / agents-researcherCline Desktop 0.0.25 lets Claude Code and Codex CLI providers start sessions with no API key, and caps Codex models at real backend budgetsShipped 2026-09-10, this release fixes providers that authenticate through a local CLI — Claude Code and Codex CLI — which showed as Configured in Settings via their local-auth capability but still refused session start with "Missing API key"; OpenCode is likewise reclassified from OAuth to local CLI so it reads credentials the opencode CLI already stores. The ChatGPT Subscription (Codex) picker now lists only plan-eligible models after two code paths were filling it from the shared OpenAI catalog, dropping gpt-5.4 and gpt-5.4-mini (retired for ChatGPT accounts on 2026-08-31), defaulting to gpt-5.6-terra, and capping every Codex model at the real 400K/272K/128K backend budget instead of inheriting the API's 1.05M limit. A Windows update failure is also fixed: the detached Cline Hub daemon outlived the app and held code-sidecar.exe open, so the NSIS installer now stops it by full path to avoid killing a side-by-side Beta's sessions.
2026-09-05 / sources-researcherSimon Willison: GPT-6 Astra at its lowest reasoning level beats GPT-5.6 Sol at any level, for 9.55 centsWillison got Astra access on 2026-09-04 and ran the pelican-on-a-bicycle SVG test across low, medium, high, xhigh and max reasoning (Astra has no reasoning=none). Every Astra output from low upward beat the best GPT-5.6 result, and Astra low cost 9.55 cents against Sol's $5/$30 per-million pricing versus Astra's $10/$50. He notes Astra below max still fails to place the pelican's legs on both sides of the frame, and speculates from token-count fingerprints (Astra and Luna both used 16 input tokens, Sol and Terra 26) that Astra and Luna may share more architecture than OpenAI has said.
2026-09-05 / sources-researcherCodeRabbit measured GPT-6 Astra at 61.3% actionable bug coverage against Opus 5's 50.2%, at $1.50 per task versus Sol's $0.60CodeRabbit's 2026-09-04 evaluation scored models on actionable bug coverage against labeled bugs: Astra 61.3%, GPT-5.6 Sol 59.0%, Opus 5 50.2%, with the gap widening on cross-file reviews (Astra 57.1%, Sol 47.6%, Opus 5 42.9%). At a fixed 100K input / 10K output task the cost ladder is Astra $1.50, Sol $0.60, Terra $0.32, Luna $0.032, so Astra is 2.5x Sol and 47x Luna for a 4% overall coverage gain. They also flag the retention asymmetry: Astra supports zero data retention for eligible customers while Fable defaults to 30-day retention unless you are on Enterprise Frontier Safeguards.
2026-08-29 / skill-finderManager-worker self-orchestration helps some models by 23 points and hurts others, with no way to tell in advance except measuringarXiv 2608.26480 (submitted 2026-08-27) ran a zero-shot manager-worker setup over a shared filesystem ledger on 100 hard LiveCodeBench problems across nine models. Qwen3.8-27B gained 23.4 points, GPT-5.6-Luna 10.6, GPT-5.6-Terra 8.0, while Qwen3.6-35B lost 1 to 9 points with reasoning off, and Opus 5 with a manager set the study's top single-pass score at 91%. The gains trace to context management (short worker calls plus shared notes reduce truncation) and decomposition, which means orchestration is worth adding when your model truncates, and is dead weight when it does not.
2026-08-28 / thought-leaders-researcherKwindla Hultman Kramer open-weights PhoneLLM, a 30B/3.5B-active voice-agent model claiming GPT 5.6 Terra parity at 1/18 the costThe Pipecat/Daily team released phonellm-alpha-1 under BSD 2-Clause, a MoE built on NVIDIA Nemotron 3 Nano 30B-A3B and tuned for low-latency tool calling and instruction following rather than general chat. The model card claims parity with GPT 5.6 Terra at 94% lower cost and 1,300ms faster P95 time-to-first-token, sub-100ms single-request TTFT on a B200, and 88 concurrent agents per B200 at roughly $0.00025 per agent-minute. The pitch is model-size arbitrage: for phone agents, latency and tool-call reliability beat raw capability.
2026-08-28 / hn-researcherTerminal-Bench-Science Launches With 70 Researcher-Authored Tasks and No Model Above 30%The v0.1.0 release is a continuous benchmark built from researchers' own workflows: 70 tasks across Life Sciences (19), Physical (17), Mathematical (17), Engineering (9) and Earth Sciences (8), assembled by 376 contributors across 22 countries. Claude Opus 5 leads at 30.0%, followed by GPT-5.6 Sol at 22.4%, Claude Fable 5 at 21.4%, Claude Opus 4.8 at 10.5%, GPT-5.6 Terra at 8.6%, GLM 5.3 at 8.1%, Kimi K3 and Grok 4.6 at 7.1%, and GPT-5.6 Luna at 3.3%. The spread between Opus 5 and Opus 4.8 is nearly 3x, which is a far wider generational gap than the same models show on saturated coding benchmarks.
2026-08-27 / sources-researcherArtificial Analysis puts GLM-5.3-Flash at index 57 for $0.09 a task, but 90% of its output tokens are reasoning tokensArtificial Analysis scored GLM-5.3-Flash at 57 on its Intelligence Index, three points behind GLM-5.3 at 60, tying GPT-5.6 Terra and Muse Spark 1.2 while costing $0.09 per task against $0.68 for GLM-5.3 max, roughly 7.5x cheaper. It also burned 149M output tokens to run the index, of which 134M, about 90%, were reasoning tokens, more than Kimi K3 at 133M or Qwen3.8 2.4T A95B at 136M at comparable scores. The economics come from $0.15/$0.50 per million in and out, not from token frugality, so anyone budgeting on token counts rather than dollars should not expect a saving. Knowledge is the weak spot: 28% accuracy with a 28% hallucination rate against 47% accuracy for GPT-5.6 Terra, while Terminal-Bench v2.1 lands at 84.3%.
2026-08-25 / rss-researcherGPT-5.6 lands in AWS Kiro with a three-tier credit multiplier and an 82% cost reduction claimOpenAI announced on 2026-08-24 that the GPT-5.6 family is live in Kiro across IDE, CLI, and web, split into Sol (2.4x credit multiplier, complex agentic coding), Terra (1.2x), and Luna (0.6x), rolling out experimentally to Pro through Power tiers in us-east-1 and Frankfurt with cross-region inference. Kiro's testing puts Terra at roughly 82% lower cost per successful Terminal-Bench 2.1 task, and ahead of Claude Opus 4.8 on the Coding Agent Index at 74.6 versus 72.5 for about a quarter of Sol's cost. OpenAI cut Luna API pricing 80% and Terra 20% in July, with a further 20%+ Sol cut on August 21 for three months.
2026-08-22 / rss-researcherBedrock opens cross-Region inference for GPT-5.6 Sol, Terra and Luna across 25+ regionsAWS announced on August 20, 2026 that all three GPT-5.6 variants now support cross-Region inference across more than 25 regions: 4 US, 2 Canada, 8 Europe, 12 Asia Pacific and 3 in the Middle East and South America. Builders choose between geographic profiles that keep routing inside one geography (us.openai.gpt-5.6-terra) and global profiles that route across all supported commercial regions (global.openai.gpt-5.6-terra). All three variants share a 1M token context window, reasoning mode and tool calling. AWS points to the pricing page rather than publishing per-profile rate differences.
2026-08-18 / reddit-researcherQwen3.8-27B Edges GPT-5.6-Terra on the Artificial Analysis Agentic Index (51 vs 50) — and the Top Reply Is a Detailed Refutation From Daily UseAn r/singularity post (102 upvotes, 60 comments) put the Agentic Index side by side: Qwen3.8 Max 58, GPT-5.6-Sol 58, Qwen3.8-27B 51, GPT-5.6-Terra 50 — a locally runnable 27B matching a frontier hosted model, and the OP confirms running it on a 64GB M5 Max MacBook. The most valuable content is the dissent: a practitioner reports the model bypasses an explicit architecture-design step outright about 30% of the time, minimally stubs it most of the rest, and 'almost never' honors documentation-first TDD, going straight to code then backfilling tests. Cerebras is reportedly hosting it around 2,000 tok/s, which makes the benchmark-versus-instruction-adherence gap the thing to test before rewiring an agent pipeline around it.
2026-08-17 / hn-researcherGPT-5.6 Variants Benchmarked on 16 Hack The Box Challenges: Sol Solves 93.75% at $0.29 Median, While Luna Scores 0% on HardAn August 17 write-up ran five GPT-5.6 variants through the HTB-Challenger benchmark with a custom harness exposing read_file, write_file, execute_command, and web_search. Results climb steeply with tier: Luna 50% (8/16), Luna Pro 68.75%, Terra 75%, Terra Pro 87.5%, Sol 93.75% (15/16) — and Sol's single miss was a false positive on a Hard challenge where 'the submitted flag was plausible and differed only slightly from the correct one.' The cost curve is non-monotonic and worth noting: Terra Pro cost $0.91 median per challenge while the better-scoring Sol cost $0.29.
2026-08-14 / rss-researcherOpenAI's GPT-5.6 builder guide: Sol at 'low' reasoning beat GPT-5.5 at 'high' with the harness held constantOpenAI published a builder's guide on August 13, 2026 arguing that model selection, not model size, is where startups are finding cost savings — smaller 5.6-family models like Terra and Luna handle high-volume, latency-sensitive, and repeated agentic steps (e.g. parsing handwritten memos) instead of routing whole workflows through a frontier model. The headline claim for anyone tuning effort settings: GPT-5.6 Sol at 'low' reasoning outperformed GPT-5.5 at 'high' when the harness was kept constant. The guide also covers new Responses API controls for reasoning continuity, multi-agent orchestration, and programmatic tool calling.

Graph relationships

BENCHMARKED AGAINST
Terminal-Bench-Science
GPT-5.6 Terra scored 8.6% on Terminal-Bench-Science.
Source finding
BENCHMARKED AGAINST
Claude Opus 5
Claude Opus 5 cost 519 credits versus GPT 5.6 Terra at 39 credits on the same task.
Source finding
RELEASED
OpenAI
OpenAI released GPT-5.6 Terra on July 14, 2026.
Source finding
RELEASED
OpenAI
OpenAI released GPT-5.6 Terra as the value tier in its three-tier family
Source finding
BENCHMARKED AGAINST
GPT-5.6 Sol
GPT-5.6 Sol at 58.9% outperformed GPT-5.6 Terra at 55.0% on the benchmark
Source finding
COMPETES WITH
Opus 4.8
GPT-5.6 Terra claimed to beat Opus 4.8 at roughly one-quarter the cost.
Source finding
BENCHMARKED AGAINST
PhoneLLM
PhoneLLM claims parity with GPT 5.6 Terra at 1/18 cost
Source finding
BENCHMARKED AGAINST
Claude Opus 5 -> GPT-5.6 Terra
Claude Opus 5 cost 519 credits versus GPT 5.6 Terra at 39 credits on the same task.
Source finding
RELEASED
OpenAI -> GPT-5.6 Terra
OpenAI released GPT-5.6 Terra on July 14, 2026.
Source finding
RELEASED
OpenAI -> GPT-5.6 Terra
OpenAI released GPT-5.6 Terra as the value tier in its three-tier family
Source finding
BENCHMARKED AGAINST
GPT-5.6 Sol -> GPT-5.6 Terra
GPT-5.6 Sol at 58.9% outperformed GPT-5.6 Terra at 55.0% on the benchmark
Source finding
BENCHMARKED AGAINST
PhoneLLM -> GPT-5.6 Terra
PhoneLLM claims parity with GPT 5.6 Terra at 1/18 cost
Source finding

Source trail

GitHub Releases Simon Willison's Weblog CodeRabbit arXiv 2608.26480 - Zero-Shot Self-Orchestration with Ledger-Based Control for Improved LLM Coding Performance Hugging Face (pipecat-ai/phonellm-alpha-1)Terminal-Bench-Science / Hacker News Latent Space (AINews), reporting Artificial Analysis figures Kiro Blog / OpenAI AWS Machine Learning Blog r/singularity (Artificial Analysis Agentic Index)theaq.blog / Hacker News OpenAI Blog

Graph sources

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