Chat turn cost
1K fresh input + 500 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
GPT-6.1 Sol vs GPT-6.1 Terra
Updated October 26, 2026. Rank says GPT-6.1 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.
Share or export
Decision reading
GPT-6.1 Sol has the higher public point estimate, 84.3 versus 75.87. Their conditional score ranges overlap. These ranges do not establish rank confidence. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 6
- GPT-6.1 Sol only
- 16
- GPT-6.1 Terra only
- 4
- Like-for-like categories
- 1 / 8
Supported: GPT-6.1 Sol · Estimated: GPT-6.1 Terra. 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.
Cache-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
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-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 Sol and GPT-6.1 Terra are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.
71.4GPT-6.1 Sol—GPT-6.1 Terra
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.
16 GPT-6.1 Sol only
6 Shared
4 GPT-6.1 Terra only
1 category rests 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.
Knowledge
Like-for-like- GPT-6.1 Sol
- 85.9
- Supported · #3/188
- GPT-6.1 Terra
- 74.9
- Supported · #12/188
- Basis
- BenchAlign v5.8 lane · 5 vs 5 public rows
- Reading
- GPT-6.1 Sol leads · intervals overlap
Agentic
Directional only- GPT-6.1 Sol
- 77.5
- Estimated · #5/133
- GPT-6.1 Terra
- 72.4
- Estimated · #8/133
- Basis
- BenchAlign v5.8 lane · 6 vs 4 public rows
- Reading
- Directional only
Coding
Not comparable- GPT-6.1 Sol
- 71.4
- Supported · #8/154
- GPT-6.1 Terra
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-6.1 Sol
- 90.5
- #2/26
- GPT-6.1 Terra
- 81.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-6.1 Sol
- 88.2
- Unranked · 1 rankable row
- GPT-6.1 Terra
- 91.6
- #4/58
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-6.1 Sol
- Not ranked
- GPT-6.1 Terra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-6.1 Sol
- Not ranked
- GPT-6.1 Terra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-6.1 Sol
- 97.3
- Unranked · 2 rankable rows
- GPT-6.1 Terra
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-6.1 Sol | GPT-6.1 Terra | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 85.9Supported · #3/188 | 74.9Supported · #12/188 | Like-for-likeBenchAlign v5.8 lane · 5 vs 5 public rows | GPT-6.1 Sol leads · intervals overlap |
| Agentic | 77.5Estimated · #5/133 | 72.4Estimated · #8/133 | Directional onlyBenchAlign v5.8 lane · 6 vs 4 public rows | Directional only |
| Coding | 71.4Supported · #8/154 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 0 public rows | Not comparable |
| Reasoning | 90.5#2/26 | 81.8Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 88.2Unranked · 1 rankable row | 91.6#4/58 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 97.3Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 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 Sol
- $0.007
- Fits in one request
- GPT-6.1 Terra
- $0.0035
- Fits in one request
GPT-6.1 Terra has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-6.1 Sol
- $0.13
- Fits in one request
- GPT-6.1 Terra
- $0.065
- Fits in one request
GPT-6.1 Terra 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 Sol
- $0.16
- Fits in one request
- GPT-6.1 Terra
- $0.08
- 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 SolGPT-6.1 Terra
Context window
Maximum documented context; output-token limits may be lower.
GPT-6.1 Sol
GPT-6.1 Terra
API model ID
GPT-6.1 Sol
gpt-6.1-sol
OpenAI GPT-6.1 Sol model documentationGPT-6.1 Terra
gpt-6.1-terra
OpenAI GPT-6.1 Terra model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-6.1 Sol
$0.1 per 1M cached input tokens
OpenAI GPT-6.1 Sol model documentationGPT-6.1 Terra
$0.05 per 1M cached input tokens
OpenAI GPT-6.1 Terra model documentationDocumented inputs
GPT-6.1 Sol
text, image
OpenAI GPT-6.1 Sol model documentationGPT-6.1 Terra
text, image
OpenAI GPT-6.1 Terra model documentationDocumented outputs
GPT-6.1 Sol
GPT-6.1 Terra
Provider availability
GPT-6.1 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API
OpenAI GPT-6.1 Sol model documentationGPT-6.1 Terra
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6.1 Terra model documentationReasoning profile
GPT-6.1 Sol
Reasoning
GPT-6.1 Terra
Reasoning
Weight access
GPT-6.1 Sol
Proprietary
GPT-6.1 Terra
Proprietary
License
GPT-6.1 Sol
Proprietary
GPT-6.1 Terra
Proprietary
Release date
GPT-6.1 Sol
2026-09-29
GPT-6.1 Terra
2026-10-26
If you are choosing between sibling variants
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GPT-6.1 Sol has the higher public point estimate, 84.3 versus 75.87. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.13 vs $0.065. Cache-heavy agent loop: $0.16 vs $0.08.
- Context tradeoff
- Both models list 1.05M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-6.1 Sol or GPT-6.1 Terra?
GPT-6.1 Sol has the higher public point estimate, 84.3 versus 75.87. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-6.1 Sol or GPT-6.1 Terra?
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 Sol or GPT-6.1 Terra?
GPT-6.1 Sol scores higher for agentic tasks on the public lane, 77.5 to 72.4. GPT-6.1 Sol and GPT-6.1 Terra are 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 Sol or GPT-6.1 Terra?
For the stated presets, chat costs $0.007 on GPT-6.1 Sol and $0.0035 on GPT-6.1 Terra; repository review costs $0.13 and $0.065; the cache-heavy agent loop costs $0.16 and $0.08. Costs use the listed standard API rates.
Which has the larger context window, GPT-6.1 Sol or GPT-6.1 Terra?
Both models list the same context window, 1.05M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence26 rows
Agentic
AutomationBench
GPT-6.1 Sol36.1%SourceGPT-6.1 Terra—Not directly comparable
Terminal-Bench-Science 0.1
GPT-6.1 Sol57.0%SourceGPT-6.1 Terra58.3%SourceGPT-6.1 Terra leads this result
ExploitGym
GPT-6.1 Sol35.1%SourceGPT-6.1 Terra—Not directly comparable
MazeBench
GPT-6.1 Sol17%SourceGPT-6.1 Terra—Not directly comparable
Hermes Index
GPT-6.1 Sol57.1%SourceGPT-6.1 Terra—Not directly comparable
Hermes Bench
GPT-6.1 Sol73.9%SourceGPT-6.1 Terra—Not directly comparable
Terminal-Bench 4.0
GPT-6.1 Sol—GPT-6.1 Terra50.30%SourceNot directly comparable
Agents' Last Exam
GPT-6.1 Sol—GPT-6.1 Terra60.0%SourceNot directly comparable
OSWorld 2.1 Offline
GPT-6.1 Sol—GPT-6.1 Terra74.2%SourceNot directly comparable
Coding
Reasoning
STEMBench
GPT-6.1 Sol2.76SourceGPT-6.1 Terra—Not directly comparable
ARC-AGI-1
GPT-6.1 Sol96.50%SourceGPT-6.1 Terra—Not directly comparable
ARC-AGI-2
GPT-6.1 Sol94.2%SourceGPT-6.1 Terra—Not directly comparable
ARC-AGI-3
GPT-6.1 Sol52.7%SourceGPT-6.1 Terra—Not directly comparable
ARC-AGI-3 Provider Adapter
GPT-6.1 Sol96.18%SourceGPT-6.1 Terra—Not directly comparable
Multimodal
Knowledge
HealthBench (raw)
GPT-6.1 Sol56.7%SourceGPT-6.1 Terra51.1%SourceGPT-6.1 Sol leads this result
HealthBench (length-adjusted)
GPT-6.1 Sol58.5%SourceGPT-6.1 Terra55.1%SourceGPT-6.1 Sol leads this result
HealthBench Professional
GPT-6.1 Sol64.2%SourceGPT-6.1 Terra62.7%SourceGPT-6.1 Sol leads this result
HealthBench Professional (raw)
GPT-6.1 Sol67.2%SourceGPT-6.1 Terra64.2%SourceGPT-6.1 Sol leads this result
HealthBench Hard
GPT-6.1 Sol36.2%SourceGPT-6.1 Terra31.4%SourceGPT-6.1 Sol leads this result
Math
26 public results · 6 shared
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Last updated October 26, 2026