Terra replaces the previous flagship for most production workloads, trading Sol's ultra multi-agent mode and pro reasoning ceiling for a large context window and explicit prompt caching. It's built for engineering teams running coding agents at scale, not researchers chasing the hardest 5% of reasoning tasks.
Terra sits in the middle of OpenAI's model lineup, scoring 72.3% on SWE-bench Verified and 68.7% on GPQA Diamond as of its July 2026 release. It matches the prior flagship's performance without Sol's ultra mode or programmatic tool calling, aimed at everyday coding and knowledge work.
Where it sits
- $5.63/M$ per 1M tokensBlended price (3:1)Lower is better#46 / 64peer median $1.70/Mvendor price, checked by HokAI
- 85 tok/stokens/sOutput speedHigher is better#21 / 39peer median 90 tok/scited: Artificial Analysis
- 72.3%% solvedSWE-bench VerifiedHigher is better#21 / 28peer median 78.3%per source, see benchmark scores
- 68.7%% correctGPQA DiamondHigher is better#39 / 44peer median 88.3%per source, see benchmark scores
Pricier than 73% of the 64 GA models with a published price, in the bottom third on SWE-bench Verified (rank 21 of 28), and one of 22 that document a zero-data-retention option.
Ranks are against GA models on HokAI that publish the same figure; ties share a rank.
Provider: OpenAI · Family: GPT-5.6
Context window: 200,000 tokens · Max output: 64,000
Input modalities: text, image · Output: text, tool-calls, code
About GPT-5.6 Terra
GPT-5.6 Terra is OpenAI's balanced GPT-5.6 tier, generally available since July 9, 2026. It delivers performance competitive with the prior flagship, at roughly half that model's cost. Designed for everyday coding, knowledge work, and production workloads where frontier capability is not required but quality and cost efficiency matter.
Terra shares the core GPT-5.6 architecture: strong reasoning, token efficiency improvements over the previous generation, cache-breakpoint-based prompt caching (30-min TTL), persisted reasoning across turns, and support for reasoning effort levels none through xhigh (pro mode and max effort reserved for Sol). It does not include ultra multi-agent mode or programmatic tool calling; those stay exclusive to Sol.
The model supports a 200K context window with 64K max output tokens. Native modalities: text and image input; text, tool-calls, and code output. Vision capabilities match Sol, but audio and video stay out of scope for this generation. Terra is reachable through the OpenAI API (Responses API and Chat Completions), ChatGPT, Codex, and Microsoft Copilot, plus other model gateways. Knowledge cutoff is June 2026.
The pricing FAQ has Terra's exact per-token numbers. Enterprise buyers get Zero Data Retention eligibility, US and EU data residency, plus the standard compliance certifications listed under governance.
Pricing
Input $2.50/M, Output $15.00/M, Cached input $0.3125/M (1.25x base). 50% of Sol pricing. No batch discount at launch. No ultra mode, no PTC, no pro mode. Available on OpenAI API, ChatGPT, Codex, Microsoft 365 Copilot, OpenRouter, Vercel, Cloudflare, Snowflake, Databricks Mosaic.
What a real job costs
| Job | Input | Output | Total |
|---|---|---|---|
| Summarise a 20-page PDF | $0.075 | $0.015 | $0.090 |
| Support reply | $0.0050 | $0.0045 | $0.0095 |
| One coding agent run | $0.500 | $0.300 | $0.800 |
Budgets: 20-page PDF = 30k in / 1k out · Support reply = 2k in / 300 out · Coding agent run = 200k in / 20k out. Computed from the vendor's per-token prices at render time; cached-input discounts are not applied.
Key Features
- Flagship-Class Performance at Half Cost: Matches the prior generation's flagship on SWE-bench, GPQA, and MMLU-Pro within 2-3 points while costing exactly half per token.
- Explicit Prompt Caching: Cache breakpoints with a 30-min TTL and writes at 1.25x the base rate, giving predictable costs for repeated-context workloads.
- Persisted Reasoning: Reuses reasoning items across turns via reasoning.context, improving multi-turn quality and cache efficiency.
- Full Reasoning Effort Range: Supports none, low, medium, high, and xhigh so compute matches task complexity without a pro-mode ceiling.
- Broad Distribution: Reachable through the OpenAI API, ChatGPT, Codex, Vercel, Cloudflare, Snowflake, and Databricks, plus Microsoft 365 Copilot.
Pros
- Best price-to-performance in the family: matches the prior flagship at exactly half its per-token cost.
- Explicit caching and persisted reasoning optimize multi-turn agent economics, which matters for production fleets.
- Faster than Sol (85 vs 75 tok/sec) with lower latency (1000ms vs 1200ms p50), better for latency-sensitive paths.
- Broad availability across the API, ChatGPT, Codex, Copilot, and gateway partners means no capacity waitlists.
- Strong safety parity with Sol, including high CBRN and cyber marks with below-high self-improvement, keeps it enterprise compliant.
Cons
- No ultra multi-agent mode, so it can't parallelize complex decomposable tasks the way Sol reduces wall-clock time on those.
- No programmatic tool calling, so tool-heavy loops need standard turn-based calling, costing more tokens and turns.
- No pro reasoning mode, so the xhigh ceiling limits quality on the hardest reasoning, where Sol's pro and max modes win on novel algorithms.
- Context tops out well below Sol's largest window; long-document workloads still need Sol or explicit caching strategy.
- Vision only for multimodal input, with no audio or video; Sol and Gemini 3.1 lead on multimodal breadth.
Benchmarks
- MATH: 82.3% vendor-reported · 26 Jun 2026 — Competition maths problems, % solved.
- MMLU: 91.4% vendor-reported · 26 Jun 2026 — General-knowledge exam across 57 subjects, % correct.
- MMLU-Pro: 81.2% vendor-reported · 26 Jun 2026 — A harder version of the 57-subject knowledge exam, % correct.
- AIME 2025: 84.1% vendor-reported · 26 Jun 2026 — Competition-level maths problems from the 2025 exam, % solved.
- ARC-AGI 2: 25.8% vendor-reported · 26 Jun 2026 — Abstract visual puzzles built to resist memorisation, % solved.
- HumanEval: 94.5% vendor-reported · 26 Jun 2026 — Small programs that must pass hidden tests, % passing.
- LiveBench: 64.3% vendor-reported · 26 Jun 2026 — A rolling set of fresh questions that cannot have been in training data, % correct.
- LMArena Elo: 1382 independent · 12 Jul 2026 — Rating from blind human votes on which answer is better.
- GPQA Diamond: 68.7% vendor-reported · 26 Jun 2026 — PhD-level science questions that are hard to search for, % correct.
- LMArena rank: #5 independent · 12 Jul 2026 — Position on the blind human-preference leaderboard; #1 is best.
- Aider Polyglot: 71.2% vendor-reported · 26 Jun 2026 — Code edits across several programming languages, % correct.
- SWE-bench Verified: 72.3% vendor-reported · 26 Jun 2026 — Real GitHub issues fixed end to end, % solved.
- Humanity's Last Exam: 18.9% vendor-reported · 26 Jun 2026 — Expert-written questions across many fields, % correct.
- AA Intelligence Index: 58 cited: Artificial Analysis · 10 Jul 2026 — Composite of 10 evaluations run by Artificial Analysis, 0 to 100.
- AA blended price: $8.75/M cited: Artificial Analysis · 10 Jul 2026 — Price per 1M tokens at a 3:1 input to output blend, as listed by Artificial Analysis.
- Output speed: 85 tok/s cited: Artificial Analysis · 10 Jul 2026 — Median tokens written per second as measured by Artificial Analysis.
A benchmark is an exam, not the job. Scores transfer unevenly between tasks, so weigh the one closest to your workload and read every figure with its source.
Frequently Asked Questions
How much does GPT-5.6 Terra cost per 1M tokens?
Terra costs $2.50 per 1M input tokens and $15.00 per 1M output tokens, with cached input at $0.3125 per 1M, a 90% discount over uncached. That's exactly half of Sol's per-token price, with no batch discount at launch.
How does GPT-5.6 Terra compare on benchmarks vs GPT-5.6 Sol?
Terra scores 72.3% on SWE-bench Verified against Sol's higher mark, and 68.7% on GPQA Diamond, trailing Sol on the hardest reasoning tasks. Sol adds ultra multi-agent mode and a pro reasoning ceiling that Terra doesn't have, but Terra matches GPT-5.5-class performance at half Sol's per-token cost.
Is GPT-5.6 Terra open source or proprietary?
Terra is proprietary. OpenAI serves it only through its own API, ChatGPT, Codex, gateway partners, and select cloud platforms. There are no downloadable weights and no open license.
Is your data used to train GPT-5.6 Terra, or retained after the request?
By default OpenAI doesn't use API inputs or outputs to train its models unless you opt in, and Terra supports Zero Data Retention for enterprise customers. On the compliance side it holds SOC2 Type II, ISO 27001, and GDPR coverage, is HIPAA eligible, and offers US or EU data residency.
When does GPT-5.6 Terra make sense to use, and when doesn't it?
Terra fits engineering teams running production coding agents and cost-sensitive API applications that don't need frontier-level reasoning. Skip it for the hardest 5% of reasoning or research tasks, long-document work past its context limit, or anything needing Sol's ultra multi-agent parallelization; route those to Sol instead.
HokAI guides covering GPT-5.6 Terra
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