Key takeaways
- OpenAI released GPT-6.1 Terra on 26 October. It replaces GPT-5.6 Terra as OpenAI's middle tier and costs US$1 per million input tokens and US$5 per million output tokens, half the old Terra price (OpenAI, via BleepingComputer).
- Moonshot released Kimi K3.1 Pro three days earlier, on 23 October. It costs US$2 / US$10 and Moonshot says the full weights will be public by 4 November (TNW, DataCamp).
- Kimi scores higher; Terra costs half as much. Opper's tracker, which carries Artificial Analysis scores, lists K3.1 Pro at 55 and Terra at 48 (Opper).
- At a typical small-product workload, Terra is about ₹1,900 a month and Kimi about ₹3,800, before GST. Prompt caching cuts both by roughly a third.
- Our advice: use Terra for everyday computer-use and office automation, and test Kimi for long agentic coding runs. If your data cannot leave India, wait for Kimi's weights and the licence before planning to self-host.
1. What shipped
GPT-6.1 Terra sits between OpenAI's larger Sol/Astra models and the smaller Luna. OpenAI pitches it at "computer use": reading a screen, then clicking, typing and moving between apps. Its examples include filling forms, logging invoices, reading technical drawings and editing video (TNW). It is live in ChatGPT Plus, Pro, Enterprise and Edu, in Codex, and in the API as gpt-6.1-terra. OpenAI says it finishes tasks more than three times faster than GPT-6 Astra and GPT-6.1 Sol. It still recommends Sol or Astra for the hardest coding work.
Kimi K3.1 Pro is a heavily post-trained upgrade of Kimi K3, a model of roughly 3 trillion parameters. The architecture is unchanged, and the gains come from more reinforcement learning (DataCamp). It is available in the Kimi apps, on an OpenAI-compatible API as kimi-k3.1-pro, and on OpenRouter. A Highspeed variant runs two to three times faster at double the price (Moonshot, via TNW).
2. Side-by-side
| GPT-6.1 Terra | Kimi K3.1 Pro | |
|---|---|---|
| Released | 26 Oct 2026 | 23 Oct 2026 |
| Context | 1.1M tokens, 128K output (Opper) | 1M tokens, flat pricing across the window |
| Price (in / out, per 1M tokens) | US$1 / US$5 | US$2 / US$10 (Highspeed: US$4 / US$20) |
| Cached input | US$0.05 | US$0.20 |
| Artificial Analysis Intelligence Index (via Opper) | 48 | 55 |
| Terminal-Bench 4.0 (vendor-reported, max effort) | 50.3% | 65.7% |
| Weights | Closed | Open weights promised by 4 Nov; licence not yet published |
| Reasoning effort | Low to max | Low / high / max (max is the API default) |
| Data retention | OpenAI enterprise and API controls | Zero Data Retention offered to API customers |
| Strongest use | Screen-driven office work, forms, documents | Long agentic coding, browsing, finance-style analysis |
Two cautions. First, the Terminal-Bench figures come from each vendor's own harness, so treat the 15-point gap as a hint, not a verdict. The Intelligence Index row applies one method to both models and is the fairer comparison. Second, on Moonshot's own table K3.1 Pro still trails the top closed models on pure reasoning tests such as HLE-Diamond (39.4%) and ProgramBench (31.5%) (DataCamp).
3. What changed inside each model
Terra got far fewer wrong answers. Artificial Analysis measured its hallucination rate on AA-Omniscience at 38%, down from 88% for GPT-5.6 Terra. At max effort it costs US$0.40 per task on the index, against US$1.40 for the old Terra. It also found that low and medium effort give the best value; higher settings cost more for little gain (Artificial Analysis). OpenAI's own error-rate test fell from 53.6% to 16.7% at low effort.
Kimi also cut false claims. On Moonshot's in-house hallucination test, the share of false claims fell from 23.4% (K3) to 8.3%. It also improved long-horizon coding from about 56% to 61.2% on Kimi Code Bench (DataCamp). These are vendor-reported numbers and have not been independently reproduced yet.
4. The security angle you should not skip
Both releases make security claims, but in different directions.
- OpenAI rates Terra "Critical" for cybersecurity under its Preparedness Framework. That is its highest cyber category, meaning the model could help find and exploit serious flaws. Terra ships with the same safeguards OpenAI built for GPT-6 Astra. OpenAI also says Terra holds up well against prompt injection and is more likely to stay within its assigned task (Help Net Security).
- Moonshot deliberately weakened Kimi's hacking skills before the open release, by removing cyber-attack material from training. On its exploit test the released model scores 81%, against 90.5% for an unfiltered checkpoint (TNW). That is still high. Once weights are public, the safeguards are only as good as whoever runs the model.
For an Indian business, the practical point is the same for both. An agent that can click and type across your apps is a new attack surface. Run computer-use agents in a separate browser profile or VM. Give them least-privilege accounts. Never let them read untrusted web pages and hold production credentials in the same session. Log every action.
5. What it costs in rupees
Take a support or internal-docs assistant using 10 million input tokens and 2 million output tokens a month, at about ₹94 per US$ (Pound Sterling Live):
| Model | No caching | 70% of input cached | Per year (cached) |
|---|---|---|---|
| GPT-6.1 Terra | US$20 ≈ ₹1,880 | US$13.35 ≈ ₹1,255 | ≈ ₹15,000 |
| Kimi K3.1 Pro | US$40 ≈ ₹3,760 | US$27.40 ≈ ₹2,576 | ≈ ₹31,000 |
| DeepSeek V4.1 Flash 1017 (for reference) | US$5.40 ≈ ₹508 | — | — |
Add 18% GST on foreign AI services: registered businesses usually pay it under reverse charge, and individuals see it on the invoice. Output tokens drive most of these bills, so capping response length and using low effort saves more than switching vendors. At this volume, both models cost less than a single day of a developer's time each month. Choose on quality and risk, not on the last ₹1,000.
6. Which one should you use?
| If you are… | Use | Why |
|---|---|---|
| Automating forms, invoices, CRM entry or document work | GPT-6.1 Terra, low/medium effort | Cheapest capable computer-use model, with lower error rates |
| Running long coding agents or repo-wide refactors | Trial Kimi K3.1 Pro next to Sol/Astra | Strong vendor-reported agentic scores at a fraction of top-tier prices |
| A bank, insurer, hospital or government vendor | Terra through your existing cloud contract, or wait | Check data residency and DPDP Act obligations first. Kimi's licence and hosting terms are not final |
| Planning to self-host in India | Wait for 4 November | Read the Kimi licence, then size the hardware. A 3T-class model needs a large multi-GPU cluster |
| A student or fresher | Both, cheaply, via OpenRouter | Build one small agent and compare cost per finished task. That makes a good portfolio project |
Whatever you pick, put a router in front so you can switch models without code changes. Then track three numbers: cost per successful task, cache hit rate and error rate on your own test set. In a month of releases like this one, those numbers matter more than any leaderboard.
Need help building or securing an AI agent? See our AI & automation development service, or compare last week's open models in Muse Spark Open vs DeepSeek V4.1 Flash.