GPT-6.1 Terra vs mainstream models
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Choose models and send one question below.Model positioning and suitable tasks
Start with the task and the serving route. The official capabilities below describe the model; the final column identifies what the current QVeris route and this text-only page actually expose. Suggested trials are editorial recommendations, not a new benchmark.
| Model | Official capability and evidence | Tasks worth testing first | Serving and page limits |
|---|---|---|---|
| GPT-6.1 Terra | Visual reasoning, screen-based work and clearer writing; the release also reports stronger terminal coding than 5.6 Terra. Release | Read invoice fields, interpret a drawing, review a small function, or turn notes into a proposal. | Official tool calling requires Responses API. The test catalog does not mark this route with tools; this page offers text only, without computer control. |
| GPT-5.6 Terra | The previous balanced everyday-work model in the 5.6 family; useful as a migration baseline. Sol-specific release scores do not describe Terra. Release | Re-run existing prompts and compare field accuracy, required edits and total cost before switching versions. | A matching context window does not imply matching capability. Keep this as a baseline rather than assuming the older version is equally strong. |
| Qwen 3.7 Plus | Official documentation highlights web development, artifacts, OCR and spatial understanding, with text, image and video input. Model description | Try structured extraction, Chinese document cleanup and frontend specifications; the price table provides a lower-cost text baseline. | This QVeris route lists text/image, not video. Qwen3.7-Max or Qwen3.8-Max scores are not Plus scores; this page has no image or video upload. |
| Kimi K3 | The release targets long-horizon coding and knowledge work, including large repositories, terminal tools and native vision. Technical release | Evaluate multi-file planning, long-document synthesis and engineering explanations with explicit acceptance checks. | The current QVeris route is text-input only. Vendor demos use an Agent environment; K3 Cluster and K3.1 Pro are different configurations/models. |
| Gemini 4 Carbon | Google’s newer Gemini generation; evaluate long-document synthesis, multimodal understanding and coding. | Run the same coding review and evidence-summary prompts. | November 11 QVeris catalog: text/image input; this page is text-only. Native output limits differ from route metadata. |
Model specifications on QVeris
Terra and the original comparison models retain the archived route-specification snapshot; Gemini 4 Carbon uses the November 11 public catalog. Native specifications and route limits are distinct. Check current availability before calling.
| Model | Context tokens | Max output tokens | Catalog input |
|---|---|---|---|
| GPT-6.1 Terra | 1,050,000 | 128,000 | image, text |
| GPT-5.6 Terra | 1,050,000 | 128,000 | text, image |
| Qwen 3.7 Plus | 1,000,000 | 65,536 | text, image |
| Kimi K3 (Agent Plan primary) | 1,048,576 | 131,072 | text |
| Gemini 4 Carbon | 1,048,576 | 943,718 | image, text |
Checked November 11, 2026: the public QVeris catalog did not return GPT-6.1 Terra. Its route specifications and Credits below are historical test-site data, not a current availability or pricing promise. Check the catalog before sending a paid request.
QVeris Model Gateway: capabilities and integration
Use QVeris to compare models across providers and connect your selected model to an application or agent. Filter the catalog by capabilities, input formats, context limits and Credits pricing before choosing a model. Browse the model catalog
| Gateway capability | How it helps your workflow |
|---|---|
| One API key and endpoint | Use a QVeris API key with the OpenAI-compatible base URL https://aigateway.qveris.ai/v1. |
| Multiple client protocols | Chat Completions, Responses, Messages and Embeddings are available when supported by the selected model. |
| Capability-based model selection | Check GET /v1/models for public model IDs and supported capabilities before switching models. |
| Streaming and usage records | Receive streaming output and billing information; retain the call ID to correlate usage and troubleshoot requests. |
Compare catalog prices before calling; use the final QVeris usage record to reconcile actual Credits. Supported interfaces and parameters vary by model. Model Gateway documentation
Multi-model API pricing and usage costs
| Model | Input tier starts | Input Credits / M | Output Credits / M | Cache-read Credits / M |
|---|---|---|---|---|
| GPT-6.1 Terra | 0+ | 650 | 3,250 | 32.5 |
| GPT-6.1 Terra | 272,000+ | 1,300 | 4,875 | 65 |
| GPT-5.6 Terra | 0+ | 1,300 | 7,800 | 130 |
| Qwen 3.7 Plus | 0+ | 208 | 832 | 41.6 |
| Qwen 3.7 Plus | 256,001+ | 624 | 2,496 | 124.8 |
| Kimi K3 (Agent Plan primary) | 0+ | 1,805.5556 | 9,027.7778 | 180.5556 |
These are test-site Credits prices checked on November 2, 2026, not vendor USD prices or a production pricing commitment. Cache hits use the cache-read rate, and long inputs use the applicable tier.
For 1,000 input and 200 output tokens without caching at base rates: Terra costs (1,000 × 650 + 200 × 3,250) / 1,000,000 = 1.30 Credits; GPT-5.6 Terra costs 2.86; Qwen 3.7 Plus 0.3744; Kimi K3 about 3.6111. Under this fixed-usage assumption Terra is about 54.5% cheaper than GPT-5.6 Terra, but costs more than Qwen. Different response lengths change actual costs.
OpenAI native API pricing
Checked November 11: OpenAI Standard pricing per million tokens is $1 input, $5 output and $0.05 cached input. Cache writes are $1.25 for 30-minute retention or $2.50 for 12 hours. Above 272K input tokens, the full request uses 2× input/cache rates and 1.5× output rates. The historical test-route tier begins at 272,000, a boundary difference. These USD rates are separate from QVeris Credits. Official documentation.
Gemini 4 Carbon: separate catalog reference
November 11, 2026 public-catalog snapshot: 1,050 input, 5,250 output and 52.5 cache-read Credits per million tokens. At 1,000 input + 200 output tokens without caching, the example costs 2.10 Credits. This is a different snapshot from the November 2 test prices above; do not interpret the difference as a controlled cost ranking. Current model entry.
GPT-6.1 Terra: strengths and use cases
GPT-6.1 Terra focuses on fast visual reasoning and precise computer use: understanding a screen or drawing, then acting in software to complete a task. Clear writing and improved coding support that workflow. Its positioning is efficient everyday execution; the most complex coding and research still favor Sol or Astra.
Native modalities and tool support
The documented Terra model accepts text and images and produces text. Native tool calling requires the Responses API. The page trial accepts text only and does not control a computer. Official documentation.
Reasoning effort, quality and cost
Native effort options are none, low, medium (default), high, xhigh and max. Compare matched tasks and supported settings rather than treating Max results as direct-answer performance. This page uses a 1,024-token output cap; Terra uses direct answers while some other models require low effort. Official documentation.
Core strengths and the evidence behind them
Visual reasoning → action
Agents’ Last Exam reports +15 percentage points at medium effort versus 5.6 Terra, at about one-third the cost. The official examples use tools. The community cases below do not independently reproduce this computer-use evaluation.
Coding → verify the effort setting
Official max-effort completion rises from 21.5% to 50.3%. In the 105-bug community test, the author reports max scores of 32 → 40.7, but xhigh scores of 20 → 11.3. The tests use different rubrics; stronger max results do not guarantee an upgrade at every setting. View community test
Efficiency → compare the finished work
The Alpine Gold report lists Terra at $4.90 / 43 min versus Opus at $31.85 / 2 h 45 min; Sol costs $3.62 / 43 min. The courtyard test costs Terra $1.23 versus Opus $14.17, while its author prefers Opus visually. Lower cost alone does not establish better output. View community test
GPT-6.1 Terra: official evaluations and real cases
What the original releases actually demonstrate
| OpenAI-reported evaluation | GPT-6.1 Terra | GPT-5.6 Terra | Conditions and interpretation |
|---|---|---|---|
| Terminal-Bench 4.0 | 50.3% | 21.5% | Maximum reasoning; terminal coding tasks. The reported difference is 28.8 percentage points. This is a vendor-reported comparison. |
| Internal factual-error test | 16.7% | 53.6% | Low reasoning; lower is better. This internal prompt set is not a universal hallucination rate. |
OpenAI uses invoice-to-accounting and drawing-to-3D workflows to illustrate visual-to-action tasks. Moonshot describes MiniTriton compiler development and GPU kernel optimization with up to 24 hours for four tasks. These are examples to inspect in the Terra release and K3 technical blog; they require tools and execution environments beyond this page.
Independent evaluations: exact model and settings
The table uses a third-party leaderboard snapshot checked on November 4, 2026. Artificial Analysis labels its index v5.0.1; the linked Agent Arena snapshot is dated November 1. Settings are shown separately because Arena uses xHigh for the older Terra row.
| Model | AA Intelligence Index v5.0.1 | Agent Arena overall rank / setting | Arena sessions |
|---|---|---|---|
| GPT-6.1 Terra | 51 · Max | 8 / 57 · Max | 19,912 |
| GPT-5.6 Terra | 32 · Max | 32 / 57 · xHigh | 44,557 |
| Qwen 3.7 Plus | N/A | 45 / 57 · not labelled | 24,493 |
| Kimi K3 | N/A | 25 / 57 · Max | 161,000 |
AA measures a fixed evaluation suite; Arena estimates model effects from real Agent sessions and multiple signals using causal tracing. Arena rank is not a question-accuracy percentage. The two scales should not be averaged. N/A means the current AA model page supplies no index value; it is not zero, and older index versions are not substituted.
The independent evidence makes Terra an upgrade candidate, not an automatic winner on every task. Public Max/xHigh results cannot predict this page’s short direct-answer trial. Use the inline comparison to check the same input, field-level correctness, elapsed time and actual Credits for your own workload.
Compare the task, output and trade-off—not just a model name. Official evaluations and five selected community tests are shown separately. This is a curated sample, not a count of all X tests.
Agents’ Last Exam includes architectural drawing → 3D model and film → time-coded restoration log. At medium effort: +15 points versus GPT-5.6 Terra at about one-third the cost. These are agent workflows with tools, not the text-only trial above.
Community tests: same task, visible differences
| Task | GPT-6.1 Terra | Other model / baseline | What changes | Evidence |
|---|---|---|---|---|
| Pixel-art table-tennis courtyard Single-file 3D scene with physics and moving characters. | $1.23. Author describes pale colors and awkward motion. | Opus 5.5: $14.17. Author favors its lighting, colors and detail. | Terra costs about 91% less; visual preference goes to Opus. See the video for the actual outputs. | Results / demo @vib3coded |
| Alpine Gold scene Same scene prompt across three models. | $4.90 · 43 min | Sol: $3.62 · 43 min; Opus 5.5: $31.85 · 2 h 45 min. | About 85% cheaper than Opus. Sol is cheaper than Terra; the author prefers Opus reflections and forest detail. | Results / demo @EnvolDev |
| Find and fix replanted bugs Two repositories, 105 replanted bugs. | max: 40.7; xhigh: 11.3; high: 9.7. | GPT-5.6 Terra: max 32; xhigh 20; high 18. | Author-reported scores: max improves by 8.7; lower settings regress. Units and complete rubric are not established by this post. | Results / demo @PawelHuryn |
| 3D house demolition Same 3D simulation prompt. | 7.5/10 · 6m20s · $0.24 | Muse Spark 1.3: 8.7/10 · 6m31s · $0.16; DeepSeek V4.1 Flash: 6.3/10 · 10m49s · $0.06. | Terra finishes 11 seconds before Muse; Muse scores 1.2 higher and costs $0.08 less. Author scores, not a standardized benchmark. | Results / demo @LatentOperatorX |
| Pelican cycling Generate a cycling pelican. | Foot motion failed. | No paired model output published in this post. | A failure example: check whether feet, pedals and wheels actually move together. It cannot establish a model ranking. | Results / demo @xmglab |
USD, timings, scores and visual judgments above are the authors’ reports, not QVeris charges or our reruns. Links open the original results and account. No invented before/after images are used; a missing comparator stays missing.
Page trial: inputs and acceptance checks
The five self-contained prompts test concurrent-order recovery, safe file paths, conflicting documents, deterministic animation and stratified experiment analysis. They are editorial exercises, not reproductions of the community results above. Check invariants, rejected inputs, source traceability, deterministic boundaries and aggregation bias respectively. No new model results are claimed.
GPT-6.1 Terra: usage guide and FAQ
Long output configuration and context limits
OpenAI documents a 1,050,000-token context and up to 128,000 output tokens. Input and output share the context budget; configure output limits for the task and supported route. This page caps each response at 1,024 tokens, including any thinking tokens, so long reports may be incomplete.
How are answers evaluated?
Your run shows response time, average throughput, input and output tokens, returned model identity and settled Credits. The X-inspired creative tasks need human review; they do not receive a made-up accuracy score. Original authors’ reports below remain separate from your live results.
Why can one panel succeed while the other fails?
Calls execute independently. Balance, availability, output limits and upstream errors can affect one side. Successful and partial answers are retained without automatic retries. A request failure is not a zero capability score.
Can I compare images or tool use?
This version compares text answers only. A catalog vision capability does not mean image upload is implemented here. Tool use and very long contexts require separate validation.
Official documentation and related reading
- OpenAI: GPT-6.1 Terra release and evaluations
- OpenAI: GPT-5.6 family positioning
- Qwen: exact Plus model description
- Moonshot: Kimi K3 technical release and case studies
- Artificial Analysis: Terra Max, Index v5.0.1
- Artificial Analysis: previous Terra Max
- Artificial Analysis: Qwen3.7 Plus
- Artificial Analysis: Kimi K3 Max
- Arena: Agent leaderboard snapshot
- Arena: evaluation methodology
- OpenAI Terra specifications and pricing
- OpenRouter exact model and routing information
- QVeris Terra model details
Related guides: Gemini 4 Carbon · Kimi K3.1 Pro · All models.