GPT-5.4 vs GPT-6.1 Terra

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.

Decision reading

GPT-6.1 Terra has the higher public point estimate, 75.87 versus 66.56. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.4

OpenAI

66.56/100

Supported · Public rank #32

90% interval 62.1–71.0

Model B

GPT-6.1 Terra

OpenAI

75.87/100

Estimated · Public rank #10

Conditional range 61.5–90.2

Shared results: 3
GPT-5.4 only: 40
GPT-6.1 Terra only: 7
Like-for-like categories: 1 / 8

Supported: GPT-5.4 · 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.

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
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-rates
Coding 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: limited
Agentic 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
Long 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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

48.0GPT-5.4—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.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

MMMU-ProMultimodal
Normalized gap 4.3
GPT-5.4:81.2%
GPT-6.1 Terra:85.5%
GPT-5.4 source GPT-6.1 Terra source

Bars run 0–100 on each benchmark’s normalized display scale

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-5.4: 68.3; Supported · #22/188
GPT-6.1 Terra: 74.9; Supported · #12/188
Basis: BenchAlign v5.8 lane · 7 vs 5 public rows
Reading: GPT-6.1 Terra leads · intervals overlap

Agentic

Directional only

GPT-5.4: 53.8; Supported · #46/133
GPT-6.1 Terra: 72.4; Estimated · #8/133
Basis: BenchAlign v5.8 lane · 15 vs 4 public rows
Reading: Directional only

Multimodal

Directional only

GPT-5.4: 71.5; #35/58
GPT-6.1 Terra: 91.6; #4/58
Basis: Provisional lane · 3 vs 1 weighted rows
Reading: Directional only

Coding

Not comparable

GPT-5.4: 48.0; Supported · #49/154
GPT-6.1 Terra: Not ranked
Basis: BenchAlign v5.8 lane · 6 vs 0 public rows
Reading: Not comparable

Reasoning

Not comparable

GPT-5.4: 64.2; #19/26
GPT-6.1 Terra: 81.8; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.4: Not ranked
GPT-6.1 Terra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.4: 90.7; #25/155
GPT-6.1 Terra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.4: 82.0; Unranked · 2 rankable rows
GPT-6.1 Terra: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.4 and GPT-6.1 Terra
Category	GPT-5.4	GPT-6.1 Terra	Basis	Reading
Knowledge	68.3Supported · #22/188	74.9Supported · #12/188	Like-for-likeBenchAlign v5.8 lane · 7 vs 5 public rows	GPT-6.1 Terra leads · intervals overlap
Agentic	53.8Supported · #46/133	72.4Estimated · #8/133	Directional onlyBenchAlign v5.8 lane · 15 vs 4 public rows	Directional only
Multimodal	71.5#35/58	91.6#4/58	Directional onlyProvisional lane · 3 vs 1 weighted rows	Directional only
Coding	48.0Supported · #49/154	Not ranked	Not comparableBenchAlign v5.8 lane · 6 vs 0 public rows	Not comparable
Reasoning	64.2#19/26	81.8Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	90.7#25/155	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	82.0Unranked · 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-5.4: $0.01; 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-5.4: $0.17; 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-5.4: $0.25; 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-5.4GPT-6.1 Terra

Context window

Maximum documented context; output-token limits may be lower.

GPT-5.4

1.05M

OpenAI pricing

GPT-6.1 Terra

1.05M

OpenAI GPT-6.1 Terra model documentation

API model ID

GPT-5.4

gpt-5.4

OpenAI pricing

GPT-6.1 Terra

gpt-6.1-terra

OpenAI GPT-6.1 Terra model documentation

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GPT-5.4

$0.25 per 1M cached input tokens

OpenAI pricing

GPT-6.1 Terra

$0.05 per 1M cached input tokens

OpenAI GPT-6.1 Terra model documentation

Documented inputs

GPT-5.4

Not sourced

GPT-6.1 Terra

text, image

OpenAI GPT-6.1 Terra model documentation

Documented outputs

GPT-5.4

Not sourced

GPT-6.1 Terra

text

OpenAI GPT-6.1 Terra model documentation

Provider availability

GPT-5.4

Not sourced

GPT-6.1 Terra

Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex

OpenAI GPT-6.1 Terra model documentation

Reasoning profile

GPT-5.4

Reasoning

GPT-6.1 Terra

Reasoning

Weight access

GPT-5.4

Proprietary

GPT-6.1 Terra

Proprietary

License

GPT-5.4

Proprietary

GPT-6.1 Terra

Proprietary

Release date

GPT-5.4

2026-03-05

GPT-6.1 Terra

2026-10-26

If you already use one of these models

Deployment change: Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal: GPT-6.1 Terra has the higher public point estimate, 75.87 versus 66.56. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: Repository review: $0.17 vs $0.065. Cache-heavy agent loop: $0.25 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-5.4 or GPT-6.1 Terra?

GPT-6.1 Terra has the higher public point estimate, 75.87 versus 66.56. 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-5.4 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-5.4 or GPT-6.1 Terra?

GPT-6.1 Terra scores higher for agentic tasks on the public lane, 72.4 to 53.8. 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-5.4 or GPT-6.1 Terra?

For the stated presets, chat costs $0.01 on GPT-5.4 and $0.0035 on GPT-6.1 Terra; repository review costs $0.17 and $0.065; the cache-heavy agent loop costs $0.25 and $0.08. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.4 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 evidence50 rows

Agentic

Terminal-Bench 2.0
GPT-5.475.1%
Source
GPT-6.1 Terra—
Not directly comparable
CyberGym
GPT-5.479.0%
Source
GPT-6.1 Terra—
Not directly comparable
BrowseComp
GPT-5.482.7%
Source
GPT-6.1 Terra—
Not directly comparable
OSWorld-Verified
GPT-5.475%
Source
GPT-6.1 Terra—
Not directly comparable
MCP Atlas
GPT-5.470.6%
Source
GPT-6.1 Terra—
Not directly comparable
Toolathlon
GPT-5.454.6%
Source
GPT-6.1 Terra—
Not directly comparable
τ²-bench results
GPT-5.498.9%
Source
GPT-6.1 Terra—
Not directly comparable
Claw-Eval
GPT-5.460.3%
Source
GPT-6.1 Terra—
Not directly comparable
DeepSearchQA
GPT-5.473.6%
Source
GPT-6.1 Terra—
Not directly comparable
Gert Labs
GPT-5.464.89%
Source
GPT-6.1 Terra—
Not directly comparable
ResearchClawBench
GPT-5.415.3%
Source
GPT-6.1 Terra—
Not directly comparable
JobBench
GPT-5.438.9%
Source
GPT-6.1 Terra—
Not directly comparable
ExploitGym
GPT-5.46.0%
Source
GPT-6.1 Terra—
Not directly comparable
ApprenticeBench
GPT-5.411%
Source
GPT-6.1 Terra—
Not directly comparable
MazeBench
GPT-5.40%
Source
GPT-6.1 Terra—
Not directly comparable
Terminal-Bench 4.0
GPT-5.4—
GPT-6.1 Terra50.30%
Source
Not directly comparable
Terminal-Bench-Science 0.1
GPT-5.4—
GPT-6.1 Terra58.3%
Source
Not directly comparable
Agents' Last Exam
GPT-5.4—
GPT-6.1 Terra60.0%
Source
Not directly comparable
OSWorld 2.1 Offline
GPT-5.4—
GPT-6.1 Terra74.2%
Source
Not directly comparable

Coding

LiveCodeBench Pro
GPT-5.487.5%
Source
GPT-6.1 Terra—
Not directly comparable
SWE-bench Pro
GPT-5.457.7%
Source
GPT-6.1 Terra—
Not directly comparable
React Native Evals
GPT-5.485.3%
Source
GPT-6.1 Terra—
Not directly comparable
Terminal-Bench 2.0
GPT-5.475.1%
Source
GPT-6.1 Terra—
Not directly comparable
Vibe Code Bench
GPT-5.467.42%
Source
GPT-6.1 Terra—
Not directly comparable
PostTrainBench v1.1
GPT-5.419.0%
Source
GPT-6.1 Terra—
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.474.0%
Source
GPT-6.1 Terra—
Not directly comparable
ARC-AGI-3
GPT-5.40.2%
Source
GPT-6.1 Terra—
Not directly comparable
ARC-AGI-1
GPT-5.493.67%
Source
GPT-6.1 Terra—
Not directly comparable

Multimodal

ParseBench
GPT-5.462.23%
Source
GPT-6.1 Terra—
Not directly comparable
MMMU-Pro
GPT-5.481.2%
Source
GPT-6.1 Terra85.5%
Source
GPT-6.1 Terra leads this result
OfficeQA Pro
GPT-5.453.2%
Source
GPT-6.1 Terra—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.482.1%
Source
GPT-6.1 Terra—
Not directly comparable
CharXiv
GPT-5.482.8%
Source
GPT-6.1 Terra—
Not directly comparable
ERQA
GPT-5.465.4%
Source
GPT-6.1 Terra—
Not directly comparable
SimpleVQA
GPT-5.461.1%
Source
GPT-6.1 Terra—
Not directly comparable
ScreenSpot Pro
GPT-5.485.4%
Source
GPT-6.1 Terra—
Not directly comparable
ZeroBench
GPT-5.441.0%
Source
GPT-6.1 Terra—
Not directly comparable
MedXpertQA (MM)
GPT-5.477.1%
Source
GPT-6.1 Terra—
Not directly comparable

Knowledge

GPQA
GPT-5.492.8%
Source
GPT-6.1 Terra—
Not directly comparable
HLE
GPT-5.452.1%
Source
GPT-6.1 Terra—
Not directly comparable
HLE w/o tools
GPT-5.439.8%
Source
GPT-6.1 Terra—
Not directly comparable
GPQA-D
GPT-5.492.8%
Source
GPT-6.1 Terra—
Not directly comparable
HealthBench Hard
GPT-5.440.1%
Source
GPT-6.1 Terra31.4%
Source
GPT-5.4 leads this result
MedXpertQA (Text)
GPT-5.459.6%
Source
GPT-6.1 Terra—
Not directly comparable
HealthBench Professional
GPT-5.448.1%
Source
GPT-6.1 Terra62.7%
Source
GPT-6.1 Terra leads this result
HealthBench (raw)
GPT-5.4—
GPT-6.1 Terra51.1%
Source
Not directly comparable
HealthBench (length-adjusted)
GPT-5.4—
GPT-6.1 Terra55.1%
Source
Not directly comparable
HealthBench Professional (raw)
GPT-5.4—
GPT-6.1 Terra64.2%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GPT-5.478.596%
Source
GPT-6.1 Terra—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.449.000%
Source
GPT-6.1 Terra—
Not directly comparable

50 public results · 3 shared

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Last updated October 26, 2026

GPT-5.4 vs GPT-6.1 Terra

Decision reading

Which one for your work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Agentic work

Long documents

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Knowledge

Agentic

Multimodal

Coding

Reasoning

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, GPT-5.4 or GPT-6.1 Terra?

Which is better for coding, GPT-5.4 or GPT-6.1 Terra?

Which is better for agentic tasks, GPT-5.4 or GPT-6.1 Terra?

Which costs less, GPT-5.4 or GPT-6.1 Terra?

Which has the larger context window, GPT-5.4 or GPT-6.1 Terra?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch GPT-5.4 vs GPT-6.1 Terra