Long documents
Prompts that approach the documented context limit
GPT-6.1 Terra
GPT-6.1 Terra has the larger documented context window.
Confidence: documented
GPT-6.1 Terra vs Kimi K2.7 Code
Updated October 26, 2026. Rank says GPT-6.1 Terra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- GPT-6.1 Terra only
- 10
- Kimi K2.7 Code only
- 13
- Like-for-like categories
- 0 / 8
Estimated: GPT-6.1 Terra · Supported: Kimi K2.7 Code. Conditional ranges do not establish rank confidence.How the comparison works
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Chat turn cost
1K fresh input + 500 output tokens
Kimi K2.7 Code
Kimi K2.7 Code has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-6.1 Terra
GPT-6.1 Terra has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Kimi K2.7 Code
Kimi K2.7 Code has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-6.1 Terra is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6.1 Terra is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
—GPT-6.1 Terra41.9Kimi K2.7 Code
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
10 GPT-6.1 Terra only
13 Kimi K2.7 Code only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Agentic
Directional only- GPT-6.1 Terra
- 72.4
- Estimated · #8/133
- Kimi K2.7 Code
- 38.5
- Supported · #75/133
- Basis
- BenchAlign v5.8 lane · 4 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-6.1 Terra
- 74.9
- Supported · #12/188
- Kimi K2.7 Code
- 53.4
- Estimated · #67/188
- Basis
- BenchAlign v5.8 lane · 5 vs 0 public rows
- Reading
- Directional only
Coding
Not comparable- GPT-6.1 Terra
- Not ranked
- Kimi K2.7 Code
- 41.9
- Supported · #64/154
- Basis
- BenchAlign v5.8 lane · 0 vs 7 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-6.1 Terra
- 81.8
- Unranked · 2 rankable rows
- Kimi K2.7 Code
- 79.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-6.1 Terra
- 91.6
- #4/58
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-6.1 Terra
- Not ranked
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-6.1 Terra
- Not ranked
- Kimi K2.7 Code
- 76.6
- #68/155
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-6.1 Terra
- Not ranked
- Kimi K2.7 Code
- 58.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | GPT-6.1 Terra | Kimi K2.7 Code | Basis | Reading |
|---|---|---|---|---|
| Agentic | 72.4Estimated · #8/133 | 38.5Supported · #75/133 | Directional onlyBenchAlign v5.8 lane · 4 vs 4 public rows | Directional only |
| Knowledge | 74.9Supported · #12/188 | 53.4Estimated · #67/188 | Directional onlyBenchAlign v5.8 lane · 5 vs 0 public rows | Directional only |
| Coding | Not ranked | 41.9Supported · #64/154 | Not comparableBenchAlign v5.8 lane · 0 vs 7 public rows | Not comparable |
| Reasoning | 81.8Unranked · 2 rankable rows | 79.5Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 91.6#4/58 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 76.6#68/155 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 58.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- GPT-6.1 Terra
- $0.0035
- Fits in one request
- Kimi K2.7 Code
- $0.00295
- Fits in one request
Kimi K2.7 Code has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-6.1 Terra
- $0.065
- Fits in one request
- Kimi K2.7 Code
- $0.0595
- Fits in one request
Kimi K2.7 Code has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-6.1 Terra
- $0.08
- Fits in one request
- Kimi K2.7 Code
- $0.097
- Fits in one request
GPT-6.1 Terra has the lower modeled cost
Costs use the listed standard API rates.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-6.1 TerraKimi K2.7 Code
Context window
Maximum documented context; output-token limits may be lower.
GPT-6.1 Terra
Kimi K2.7 Code
256K
API model ID
GPT-6.1 Terra
gpt-6.1-terra
OpenAI GPT-6.1 Terra model documentationKimi K2.7 Code
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-6.1 Terra
$0.05 per 1M cached input tokens
OpenAI GPT-6.1 Terra model documentationKimi K2.7 Code
$0.19 per 1M cached input tokens
Documented inputs
GPT-6.1 Terra
text, image
OpenAI GPT-6.1 Terra model documentationKimi K2.7 Code
Not sourced
Documented outputs
GPT-6.1 Terra
Kimi K2.7 Code
Not sourced
Provider availability
GPT-6.1 Terra
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6.1 Terra model documentationKimi K2.7 Code
Not sourced
Reasoning profile
GPT-6.1 Terra
Reasoning
Kimi K2.7 Code
Reasoning
Weight access
GPT-6.1 Terra
Proprietary
Kimi K2.7 Code
Open Weight
License
GPT-6.1 Terra
Proprietary
Kimi K2.7 Code
Open Weight
Release date
GPT-6.1 Terra
2026-10-26
Kimi K2.7 Code
2026-06-12
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.065 vs $0.0595. Cache-heavy agent loop: $0.08 vs $0.097.
- Context tradeoff
- GPT-6.1 Terra has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-6.1 Terra or Kimi K2.7 Code?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-6.1 Terra or Kimi K2.7 Code?
GPT-6.1 Terra is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-6.1 Terra or Kimi K2.7 Code?
GPT-6.1 Terra scores higher for agentic tasks on the public lane, 72.4 to 38.5. GPT-6.1 Terra is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-6.1 Terra or Kimi K2.7 Code?
For the stated presets, chat costs $0.0035 on GPT-6.1 Terra and $0.00295 on Kimi K2.7 Code; repository review costs $0.065 and $0.0595; the cache-heavy agent loop costs $0.08 and $0.097. Costs use the listed standard API rates.
Which has the larger context window, GPT-6.1 Terra or Kimi K2.7 Code?
GPT-6.1 Terra has the larger documented context window: 1.05M, compared with 256K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GPT-6.1 Terra
API / mo$4,500
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.7 Code
API / mo$3,713
Self-host / mo$27,962
Break-even500M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence23 rows
Agentic
Terminal-Bench 4.0
GPT-6.1 Terra50.30%SourceKimi K2.7 Code—Not directly comparable
Terminal-Bench-Science 0.1
GPT-6.1 Terra58.3%SourceKimi K2.7 Code—Not directly comparable
Agents' Last Exam
GPT-6.1 Terra60.0%SourceKimi K2.7 Code—Not directly comparable
OSWorld 2.1 Offline
GPT-6.1 Terra74.2%SourceKimi K2.7 Code—Not directly comparable
Kimi Claw 24/7
GPT-6.1 Terra—Kimi K2.7 Code46.9%SourceNot directly comparable
MCP Atlas
GPT-6.1 Terra—Kimi K2.7 Code76%SourceNot directly comparable
MCP Mark Verified
GPT-6.1 Terra—Kimi K2.7 Code81.1%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GPT-6.1 Terra—Kimi K2.7 Code67.0%SourceNot directly comparable
Coding
Kimi Code Bench v2
GPT-6.1 Terra—Kimi K2.7 Code62.0%SourceNot directly comparable
ProgramBench
GPT-6.1 Terra—Kimi K2.7 Code53.6%SourceNot directly comparable
MLS-Bench Lite
GPT-6.1 Terra—Kimi K2.7 Code35.1%SourceNot directly comparable
CursorBench 3.2
GPT-6.1 Terra—Kimi K2.7 Code49.7%SourceNot directly comparable
OpenHarmony Bench
GPT-6.1 Terra—Kimi K2.7 Code52.1%SourceNot directly comparable
LiveCodeBench (Vals)
GPT-6.1 Terra—Kimi K2.7 Code82.1%SourceNot directly comparable
SWE-bench (Vals)
GPT-6.1 Terra—Kimi K2.7 Code78.2%SourceNot directly comparable
Multimodal
MMMU-Pro
GPT-6.1 Terra85.5%SourceKimi K2.7 Code—Not directly comparable
Knowledge
HealthBench (raw)
GPT-6.1 Terra51.1%SourceKimi K2.7 Code—Not directly comparable
HealthBench (length-adjusted)
GPT-6.1 Terra55.1%SourceKimi K2.7 Code—Not directly comparable
HealthBench Professional
GPT-6.1 Terra62.7%SourceKimi K2.7 Code—Not directly comparable
HealthBench Professional (raw)
GPT-6.1 Terra64.2%SourceKimi K2.7 Code—Not directly comparable
HealthBench Hard
GPT-6.1 Terra31.4%SourceKimi K2.7 Code—Not directly comparable
Math
23 public results · 0 shared
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Last updated October 26, 2026