GPT-5.6 Terra vs Phi-4-reasoning

Updated October 14, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 1 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-5.6 Terra

OpenAI

69.78/100

Supported · Public rank #16

90% interval 66.0–73.6

Model B

Phi-4-reasoning

Microsoft

—

Evidence status unavailable

90% interval unavailable

Shared results: 1
GPT-5.6 Terra only: 33
Phi-4-reasoning only: 5
Like-for-like categories: 0 / 8

Supported: GPT-5.6 TerraHow 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.

Long documents
Prompts that approach the documented context limit
GPT-5.6 Terra
GPT-5.6 Terra has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Phi-4-reasoning 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
Phi-4-reasoning is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Phi-4-reasoning does not fit this workload in one request. Phi-4-reasoning has no comparable published API token rate.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Phi-4-reasoning does not fit this workload in one request. Phi-4-reasoning has no comparable published API token rate.
Confidence: listed-rates

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.

62.2GPT-5.6 Terra—Phi-4-reasoning

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.

GPQAKnowledge
Normalized gap 27.1
GPT-5.6 Terra:92.9%
Phi-4-reasoning:65.8%
GPT-5.6 Terra source Phi-4-reasoning 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.

Agentic

Not comparable

GPT-5.6 Terra: 58.9; Supported · #27/124
Phi-4-reasoning: Not ranked
Basis: BenchAlign v5.8 lane · 9 vs 0 public rows
Reading: Not comparable

Coding

Not comparable

GPT-5.6 Terra: 62.2; Supported · #14/149
Phi-4-reasoning: Not ranked
Basis: BenchAlign v5.8 lane · 9 vs 1 public rows
Reading: Not comparable

Reasoning

Not comparable

GPT-5.6 Terra: 69.1; #23/29
Phi-4-reasoning: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.6 Terra: 84.5; #11/54
Phi-4-reasoning: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

GPT-5.6 Terra: 69.9; Supported · #17/181
Phi-4-reasoning: Not ranked
Basis: BenchAlign v5.8 lane · 8 vs 2 public rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.6 Terra: Not ranked
Phi-4-reasoning: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.6 Terra: 87.1; #38/128
Phi-4-reasoning: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.6 Terra: 90.1; Unranked · 3 rankable rows
Phi-4-reasoning: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.6 Terra and Phi-4-reasoning
Category	GPT-5.6 Terra	Phi-4-reasoning	Basis	Reading
Agentic	58.9Supported · #27/124	Not ranked	Not comparableBenchAlign v5.8 lane · 9 vs 0 public rows	Not comparable
Coding	62.2Supported · #14/149	Not ranked	Not comparableBenchAlign v5.8 lane · 9 vs 1 public rows	Not comparable
Reasoning	69.1#23/29	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multimodal	84.5#11/54	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Knowledge	69.9Supported · #17/181	Not ranked	Not comparableBenchAlign v5.8 lane · 8 vs 2 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	87.1#38/128	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	90.1Unranked · 3 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-5.6 Terra: $0.008; Fits in one request
Phi-4-reasoning: API rate not published; Fits in one request

Phi-4-reasoning has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Terra: $0.136; Fits in one request
Phi-4-reasoning: API rate not published; Does not fit in one request

Phi-4-reasoning does not fit this workload in one request. Phi-4-reasoning has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-5.6 Terra: $0.2; Fits in one request
Phi-4-reasoning: API rate not published; Does not fit in one request; Cached-input rate unavailable

Phi-4-reasoning does not fit this workload in one request. Phi-4-reasoning has no comparable published API token rate.

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-5.6 TerraPhi-4-reasoning

Context window

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

GPT-5.6 Terra

1.05M

OpenAI model catalog

Phi-4-reasoning

32K

API model ID

GPT-5.6 Terra

gpt-5.6-terra

OpenAI model catalog

Phi-4-reasoning

Not sourced

Cached-input rate

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

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Phi-4-reasoning

No comparable hosted API rate

Documented inputs

GPT-5.6 Terra

text, image

OpenAI model catalog

Phi-4-reasoning

Not sourced

Documented outputs

GPT-5.6 Terra

text

OpenAI model catalog

Phi-4-reasoning

Not sourced

Provider availability

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Phi-4-reasoning

Not sourced

Reasoning profile

GPT-5.6 Terra

Reasoning

Phi-4-reasoning

Reasoning

Weight access

GPT-5.6 Terra

Proprietary

Phi-4-reasoning

Open Weight

License

GPT-5.6 Terra

Proprietary

Phi-4-reasoning

Open Weight

Release date

GPT-5.6 Terra

2026-07-09

Phi-4-reasoning

2025-04-30

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: At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.6 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-5.6 Terra or Phi-4-reasoning?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-5.6 Terra or Phi-4-reasoning?

Phi-4-reasoning is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GPT-5.6 Terra or Phi-4-reasoning?

Phi-4-reasoning is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-5.6 Terra or Phi-4-reasoning?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-5.6 Terra or Phi-4-reasoning?

GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 32K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence39 rows

Agentic

Terminal-Bench 3.0
GPT-5.6 Terra20.8%
Source
Phi-4-reasoning—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Terra87.4%
Source
Phi-4-reasoning—
Not directly comparable
BrowseComp
GPT-5.6 Terra87.5%
Source
Phi-4-reasoning—
Not directly comparable
OSWorld 2.0
GPT-5.6 Terra50.2%
Source
Phi-4-reasoning—
Not directly comparable
CyberGym
GPT-5.6 Terra81.8%
Source
Phi-4-reasoning—
Not directly comparable
ExploitGym
GPT-5.6 Terra23.2%
Source
Phi-4-reasoning—
Not directly comparable
Toolathlon
GPT-5.6 Terra53.1%
Source
Phi-4-reasoning—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Terra77.5%
Source
Phi-4-reasoning—
Not directly comparable
ApprenticeBench
GPT-5.6 Terra16%
Source
Phi-4-reasoning—
Not directly comparable

Coding

SWE-bench Pro
GPT-5.6 Terra63.4%
Source
Phi-4-reasoning—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Terra87.4%
Source
Phi-4-reasoning—
Not directly comparable
DeepSWE
GPT-5.6 Terra69.6%
Source
Phi-4-reasoning—
Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Terra55.8%
Source
Phi-4-reasoning—
Not directly comparable
CursorBench 3.2
GPT-5.6 Terra64.9%
Source
Phi-4-reasoning—
Not directly comparable
VulcanBench v3
GPT-5.6 Terra87.0%
Source
Phi-4-reasoning—
Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Terra85.9%
Source
Phi-4-reasoning—
Not directly comparable
SWE-bench (Vals)
GPT-5.6 Terra95.4%
Source
Phi-4-reasoning—
Not directly comparable
CursorBench 4.0
GPT-5.6 Terra41.3%
Source
Phi-4-reasoning—
Not directly comparable
LiveCodeBench
GPT-5.6 Terra—
Phi-4-reasoning53.8%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.6 Terra83.9%
Source
Phi-4-reasoning—
Not directly comparable
ARC-AGI-3
GPT-5.6 Terra0.8%
Source
Phi-4-reasoning—
Not directly comparable
ARC-AGI-1
GPT-5.6 Terra96.50%
Source
Phi-4-reasoning—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.6 Terra80.7%
Source
Phi-4-reasoning—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.6 Terra82%
Source
Phi-4-reasoning—
Not directly comparable

Knowledge

GPQA
GPT-5.6 Terra92.9%
Source
Phi-4-reasoning65.8%
Source
GPT-5.6 Terra leads this result
GPQA-D
GPT-5.6 Terra92.9%
Source
Phi-4-reasoning—
Not directly comparable
HLE-Verified
GPT-5.6 Terra51.1%
Source
Phi-4-reasoning—
Not directly comparable
LABBench2
GPT-5.6 Terra81.2%
Source
Phi-4-reasoning—
Not directly comparable
HealthBench Professional
GPT-5.6 Terra57.7%
Source
Phi-4-reasoning—
Not directly comparable
HealthBench Hard
GPT-5.6 Terra32.7%
Source
Phi-4-reasoning—
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Terra90.9%
Source
Phi-4-reasoning—
Not directly comparable
MMLU-Pro (Vals)
GPT-5.6 Terra86.7%
Source
Phi-4-reasoning—
Not directly comparable
MMLU-Pro
GPT-5.6 Terra—
Phi-4-reasoning74.3%
Source
Not directly comparable

Instruction following

IFEval
GPT-5.6 Terra—
Phi-4-reasoning83.4%
Source
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.6 Terra84.9%
Source
Phi-4-reasoning—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Terra84.900%
Source
Phi-4-reasoning—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Terra68.300%
Source
Phi-4-reasoning—
Not directly comparable
AIME 2024
GPT-5.6 Terra—
Phi-4-reasoning75.3%
Source
Not directly comparable
AIME 2025
GPT-5.6 Terra—
Phi-4-reasoning62.9%
Source
Not directly comparable

39 public results · 1 shared

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Last updated October 14, 2026

GPT-5.6 Terra vs Phi-4-reasoning

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Reasoning

Multimodal

Knowledge

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-5.6 Terra or Phi-4-reasoning?

Which is better for coding, GPT-5.6 Terra or Phi-4-reasoning?

Which is better for agentic tasks, GPT-5.6 Terra or Phi-4-reasoning?

Which costs less, GPT-5.6 Terra or Phi-4-reasoning?

Which has the larger context window, GPT-5.6 Terra or Phi-4-reasoning?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch GPT-5.6 Terra vs Phi-4-reasoning