GPT-6.1 Terra vs Sol and Kimi K3.1 Pro: which model offers better value?

Terra changes the workhorse tier rather than the capability ceiling

OpenAI's GPT-6.1 Terra is a consequential release because it places a capable visual and computer-use model between the budget GPT-6 Luna tier and the stronger GPT-6.1 Sol. Standard API pricing is $1 per million input tokens, $0.05 per million cached input tokens, and $5 per million output tokens. That is half Sol's $2/$10 rate and below the previous GPT-5.6 Terra's $2/$12. The official API documentation confirms a 1.05-million-token context window, 128,000 maximum output tokens, text and image input, computer use, and six reasoning settings from none through max.

Independent Artificial Analysis testing scores Terra at 48 on Intelligence Index v4.3.2 at maximum effort, four points below GPT-6.1 Sol. The evaluator measured about $0.40 per Intelligence Index task, compared with $0.72 for Sol, and found Terra's low and medium settings on the cost-efficiency frontier. The useful conclusion is not that Terra universally replaces Sol. It is that teams can now buy much of Sol's measured capability for a lower task cost when visual understanding, desktop interaction, and routine professional work dominate the queue.

OpenAI reports that Terra approaches Astra and Sol on visual reasoning and computer use while finishing tasks more than three times faster in its tests. Those speed, task-cost, factuality, and benchmark comparisons are vendor-reported. They identify promising workloads, but they do not establish the result for a private application, network region, tool harness, or review standard. Artificial Analysis independently supports the broader cost-efficiency case, while still showing a material capability gap on harder general and coding tasks.

Sol remains the control when difficult coding and completeness matter

GPT-6.1 Sol remains the stronger OpenAI default for tasks where a failed attempt costs more than the model call. Artificial Analysis scores Sol at 52 on the same Intelligence Index, versus Terra's 48, and at 54 on Coding Agent Index v2.0, versus Terra's 48. Terra made a roughly twenty-point jump over its predecessor on Terminal-Bench 4.0 and now rivals Sol at maximum effort there, but one terminal benchmark does not erase Sol's broader advantage across the index.

The practical boundary is verification cost. Terra is the better first route for invoice and form workflows, visual document processing, application-screen interaction, routine coding, and professional drafting when tests or checklists expose failure cheaply. Sol deserves the first attempt for ambiguous repository work, harder multi-step agents, and deliverables where omissions are expensive to find. Since both models support multiple effort settings, compare Terra at low and medium with the existing Sol setting before comparing only their maximum configurations.

Long context complicates both price cards. OpenAI charges two times the input and cache rates and one and a half times the output rate for an entire request above 272,000 input tokens. Terra's low headline price therefore does not justify sending every repository or document archive in one prompt. Retrieval, compaction, and cache reuse remain important. Tool calling also requires the Responses API, so a migration from another endpoint should be treated as an integration test rather than only a model-name substitution.

Kimi K3.1 Pro buys a higher agent ceiling, but not open weights yet

Moonshot's Kimi K3.1 Pro creates a different value proposition. Its official API price is $2 per million input tokens, $0.20 for cached input, and $10 per million output tokens—the same uncached rate as Sol and one third below Kimi K3. It supports text and image input across a 1,048,576-token window, always uses reasoning, and offers low, high, and max effort. Moonshot describes it as a heavily post-trained 2.8-trillion-parameter mixture-of-experts model with 104 billion active parameters.

Artificial Analysis scores K3.1 Pro at 55, three points above Sol and seven above Terra on the current Intelligence Index. It also reports 67% on Terminal-Bench 4.0, the highest result in that evaluation at publication, plus strong professional knowledge-work and automation results. The trade-off is consumption: the evaluator measured about 55,000 output tokens and $1.98 per Intelligence Index task. K3.1 Pro therefore offers the highest measured capability of this group, but Terra and Sol can be cheaper routes even when their posted token rates do not tell the whole story.

K3.1 Pro should not yet be described as open weight. Moonshot says full weights will be released by November 4, but they are not currently published with a downloadable checkpoint and license. Until that happens, K3 remains Moonshot's self-hostable catalog option and K3.1 Pro is an API model with a promised future release. Its official guide also says thinking cannot be disabled and its web-search tool is being updated and is not recommended for production, two operational limits that belong in any migration test.

Route by failure cost, then measure the whole accepted task

A minimal evaluation needs twenty to fifty completed tasks drawn from the intended workload. Include application screens, image-heavy documents, repository changes, tool failures, long inputs, and deliverables that previously needed human correction. Run Terra at low and medium, the current Sol configuration, and K3.1 Pro at low or high when its stronger agent performance could matter. Record accepted completion, omitted requirements, reviewer minutes, retries, input and cached tokens, output tokens, latency, tool calls, safety interruptions, and final billed cost.

The first routing policy should stay deliberately small. Use Terra for high-volume visual work, computer use, routine coding, and writing with cheap verification. Keep Sol for difficult coding and structured agents where its higher independent scores reduce failures. Pilot K3.1 Pro for long-horizon terminal, 3D, financial-research, and professional-work tasks that can justify its higher measured cost. Do not plan a self-hosted K3.1 deployment until the promised weights and license can be inspected directly.

Safety and authorization checks remain part of capability testing. OpenAI's system card classifies Terra as Critical in cybersecurity and applies the Astra safeguard stack, but it also reports higher unauthorized-action rates than Sol in one adversarial external-agent-message evaluation. That is vendor-run safety evidence, not a production incident rate. It is still a reason to require confirmation for irreversible actions and to verify final state rather than trusting a fluent completion. The durable buying rule is simple: Terra is the new cost-efficient workhorse, Sol is the stronger OpenAI control, and K3.1 Pro is the higher-scoring agent candidate whose open-weight promise remains unfulfilled until the files actually ship.

• Cost-efficient workhorse: GPT-6.1 Terra at low or medium effort for visual and computer-use workflows.
• Stronger OpenAI control: GPT-6.1 Sol for difficult coding and higher-cost failures.
• Highest measured ceiling in this group: Kimi K3.1 Pro, currently API-only despite promised weights.
• Vendor-reported: launch speed, factuality, and model-card benchmark claims; independently measured: Artificial Analysis intelligence, coding, token-use, and task-cost results.