GPT-6.1 Terra vs MAI-Thinking-1
OpenAI · US | Microsoft · US · Updated June 2026
Quick verdict
Pick GPT-6.1 Terra for $1/$5 per million tokens (cached input $0.05) - half the price of gpt-6.1 sol and gpt-5.6 terra or terminal-bench 4.0 of 50.3% at maximum reasoning effort, versus 21.5% for gpt-5.6 terra (openai-reported). Pick MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%) or microsoft's first in-house flagship reasoner, trained without openai distillation. On a tight budget at scale, MAI-Thinking-1 is the value pick.
GPT-6.1 Terra (OpenAI) and MAI-Thinking-1 (Microsoft) are two of the models people most often weigh against each other in 2026. GPT-6.1 Terra is openAI's October 26, 2026 mid-tier model - a 1.05M-context, computer-use-capable GPT at $1/$5 per million tokens, half the price of its predecessor. MAI-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: GPT-6.1 Terra holds 4.1× more — 1.05M tokens (~1,575 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: GPT-6.1 Terra is the newer model by about 3 months (released October 26, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | GPT-6.1 Terra | MAI-Thinking-1 |
|---|---|---|
| Provider | OpenAI (US) | Microsoft (US) |
| Released | October 26, 2026 | August 12, 2026 |
| Context window | 1.05M tokens (~1,575 pages) | 256K (~384 pages) |
| Price (in/out) | $1/$5 per 1M tokens | Not published |
| Open weight? | No — API only | No — API only |
| Modalities | text, image, code | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
$1/$5 per million tokens (cached input $0.05) - half the price of GPT-6.1 Sol and GPT-5.6 Terra
GPT-6.1 Terra
Its 1.05M tokens window holds about 4.1× more than MAI-Thinking-1's 256K in a single prompt.
Terminal-Bench 4.0 of 50.3% at maximum reasoning effort, versus 21.5% for GPT-5.6 Terra (OpenAI-reported)
GPT-6.1 Terra
MAI-Thinking-1 is comparatively weak here — benchmarks are largely self-reported
1.05M-token context with up to 128K output tokens, plus image input and computer use via the Responses API
GPT-6.1 Terra
OpenAI's October 26, 2026 mid-tier model - a 1.05M-context, computer-use-capable GPT at $1/$5 per million tokens, half the price of its predecessor — and it carries the larger 1.05M tokens context.
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%)
MAI-Thinking-1
MAI-Thinking-1 lists very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%) among its strengths; GPT-6.1 Terra does not.
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation
MAI-Thinking-1
GPT-6.1 Terra is comparatively weak here — openAI still positions Sol and Astra as the higher-capability choices for demanding coding problems
Efficient reasoning at low token cost for its class
MAI-Thinking-1
GPT-6.1 Terra is comparatively weak here — requests above 272K input tokens are billed at 2x input and 1.5x output for the whole request
Lowest cost at scale
MAI-Thinking-1
Its weights are open, so at volume you pay for your own hardware instead of GPT-6.1 Terra's $1/$5 per 1M tokens.
Largest single-prompt input
GPT-6.1 Terra
Its 1.05M tokens window is about 4.1× larger than MAI-Thinking-1's 256K, fitting roughly 1,575 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MAI-Thinking-1
At Not published it undercuts GPT-6.1 Terra, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-6.1 Terra
Larger 1.05M tokens window fits more in one prompt.
Anyone whose priority is $1/$5 per million tokens (cached input $0.05) - half the price of gpt-6.1 sol and gpt-5.6 terra
→ GPT-6.1 Terra
It is specifically built for that.
Anyone whose priority is very strong math reasoning (aime 2025 97%, aime 2026 94.5%)
→ MAI-Thinking-1
That is its strongest area.
GPT-6.1 Terra: where it fits
OpenAI's October 26, 2026 mid-tier model - a 1.05M-context, computer-use-capable GPT at $1/$5 per million tokens, half the price of its predecessor. Released October 26, 2026 by OpenAI, it is built for $1/$5 per million tokens (cached input $0.05) - half the price of GPT-6.1 Sol and GPT-5.6 Terra, terminal-Bench 4.0 of 50.3% at maximum reasoning effort, versus 21.5% for GPT-5.6 Terra (OpenAI-reported), 1.05M-token context with up to 128K output tokens, plus image input and computer use via the Responses API, and error rate on OpenAI's hard-prompt set falls from 53.6% (GPT-5.6 Terra) to 16.7% at low effort and 4.3% at max effort.
Its trade-offs are real: openAI still positions Sol and Astra as the higher-capability choices for demanding coding problems, requests above 272K input tokens are billed at 2x input and 1.5x output for the whole request, closed weights and no fine-tuning support, and benchmark figures are vendor-reported; no SWE-bench Verified or MRCR numbers published. At $1 in / $5 out per million tokens, it sits in the budget price band.
MAI-Thinking-1: where it fits
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Released August 12, 2026 by Microsoft, it is built for very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%), microsoft's first in-house flagship reasoner, trained without OpenAI distillation, efficient reasoning at low token cost for its class, and competitive with Claude Opus 4.6 on SWE-Bench Pro (vendor-reported).
Its trade-offs: closed and in private preview — no open weights, no published pricing, thin availability, and benchmarks are largely self-reported.
The bottom line for this matchup
GPT-6.1 Terra and MAI-Thinking-1 overlap enough that the right pick depends on your specific job. MAI-Thinking-1 costs less per token; GPT-6.1 Terra holds the larger context; and each leads in its own area — GPT-6.1 Terra for $1/$5 per million tokens (cached input $0.05) - half the price of gpt-6.1 sol and gpt-5.6 terra, MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GPT-6.1 Terra and MAI-Thinking-1 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is GPT-6.1 Terra or MAI-Thinking-1 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, GPT-6.1 Terra leans toward $1/$5 per million tokens (cached input $0.05) - half the price of gpt-6.1 sol and gpt-5.6 terra while MAI-Thinking-1 leans toward very strong math reasoning (aime 2025 97%, aime 2026 94.5%), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-6.1 Terra or MAI-Thinking-1?
MAI-Thinking-1 is cheaper — $1/$5 per 1M tokens vs Not published.
Which has the bigger context window?
GPT-6.1 Terra — 1.05M tokens vs 256K, about 4.1× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-6.1 Terra and MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you GPT-6.1 Terra, MAI-Thinking-1 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GPT-6.1 Terra or MAI-Thinking-1?
GPT-6.1 Terra — released October 26, 2026, about 3 months after MAI-Thinking-1.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.