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BenchLM

GPT-5.6 Sol vs Phi-4 Multimodal Instruct

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.

OpenAI logo
Model A
GPT-5.6 Sol

OpenAI

80.46/100

Supported · Public rank #6

90% interval 77.6–83.3

Microsoft logo
Model B
Phi-4 Multimodal Instruct

Microsoft

—

Evidence status unavailable

90% interval unavailable

Updated September 18, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

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

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    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

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    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.

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

74.3GPT-5.6 Sol—Phi-4 Multimodal Instruct

Not comparable · BenchAlign

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 leaderboardShortlist models for this job

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
GPT-5.6 Sol only
37
Phi-4 Multimodal Instruct only
0
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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 Sol
69.8
Supported · #6/154
Phi-4 Multimodal Instruct
Not ranked
Basis
BenchAlign lane · 9 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
GPT-5.6 Sol
74.3
Supported · #5/154
Phi-4 Multimodal Instruct
Not ranked
Basis
BenchAlign lane · 12 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.6 Sol
70.4
#12/20
Phi-4 Multimodal Instruct
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.6 Sol
80.5
Supported · #5/184
Phi-4 Multimodal Instruct
Not ranked
Basis
BenchAlign lane · 8 vs 0 public rows
Reading
Not comparable

Math

Not comparable
GPT-5.6 Sol
96.8
Unranked · 3 rankable rows
Phi-4 Multimodal Instruct
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Sol
Not ranked
Phi-4 Multimodal Instruct
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.6 Sol
87.6
#4/48
Phi-4 Multimodal Instruct
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Sol
87.7
#28/124
Phi-4 Multimodal Instruct
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

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 Sol
$0.014
Fits in one request
Phi-4 Multimodal Instruct
API rate not published
Fit state unavailable

Phi-4 Multimodal Instruct has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Sol
$0.26
Fits in one request
Phi-4 Multimodal Instruct
API rate not published
Fit state unavailable

Phi-4 Multimodal Instruct has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.6 Sol
$0.36
Fits in one request
Phi-4 Multimodal Instruct
API rate not published
Fit state unavailable
Cached-input rate unavailable

Phi-4 Multimodal Instruct has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

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

GPT-5.6 Sol

1.05M

OpenAI model catalog

Phi-4 Multimodal Instruct

N/A

Cached-input rate

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

GPT-5.6 Sol

$0.4 per 1M cached input tokens

OpenAI pricing

Phi-4 Multimodal Instruct

No comparable hosted API rate

Microsoft model documentation

Provider availability

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

Phi-4 Multimodal Instruct

Not sourced

Reasoning profile

GPT-5.6 Sol

Reasoning

Phi-4 Multimodal Instruct

Non-Reasoning

Weight access

GPT-5.6 Sol

Proprietary

Phi-4 Multimodal Instruct

Open Weight

License

GPT-5.6 Sol

Proprietary

Phi-4 Multimodal Instruct

Open Weight

Release date

GPT-5.6 Sol

2026-07-09

Phi-4 Multimodal Instruct

2025-03-03

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
A complete documented context comparison is not available.

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

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

Browse raw public benchmark evidence37 rows

Agentic

  • Terminal-Bench 3.0

    GPT-5.6 Sol34.6%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.6 Sol91.9%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • BrowseComp

    GPT-5.6 Sol92.2%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • OSWorld 2.0

    GPT-5.6 Sol62.6%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • CyberGym

    GPT-5.6 Sol84.5%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Sol33.7%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Sol58%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GPT-5.6 Sol85.8%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • ApprenticeBench

    GPT-5.6 Sol26%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

Coding

  • Bug Hunt Bench

    GPT-5.6 Sol42 fixes
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • SWE-bench Pro

    GPT-5.6 Sol64.6%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.6 Sol91.9%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • DeepSWE

    GPT-5.6 Sol72.7%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.6 Sol60.6%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • FrontierSWE v2

    GPT-5.6 Sol32.2%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • cursorBench32

    GPT-5.6 Sol67.2%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • VulcanBench v3

    GPT-5.6 Sol87.0%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • VulcanBench CII v1

    GPT-5.6 Sol86.5%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • LiveCodeBench (Vals)

    GPT-5.6 Sol82.6%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • SWE-bench (Vals)

    GPT-5.6 Sol96.2%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • cursorBench40

    GPT-5.6 Sol41.7%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Sol92.5%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.6 Sol7.8%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • GeneBench-Pro

    GPT-5.6 Sol28.7%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Sol94.6%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • GPQA-D

    GPT-5.6 Sol94.6%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • HLE-Verified

    GPT-5.6 Sol54.5%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • LABBench2

    GPT-5.6 Sol82.1%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • HealthBench Professional

    GPT-5.6 Sol60.5%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Sol33.1%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • GPQA Diamond (Vals)

    GPT-5.6 Sol95.2%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • MMLU-Pro (Vals)

    GPT-5.6 Sol89.1%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Sol89%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Sol89.000%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Sol83.000%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.6 Sol83%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.6 Sol84.6%
    Source
    Phi-4 Multimodal Instruct—

    Not directly comparable

Questions

Which is better, GPT-5.6 Sol or Phi-4 Multimodal Instruct?

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-5.6 Sol or Phi-4 Multimodal Instruct?

Phi-4 Multimodal Instruct 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 Sol or Phi-4 Multimodal Instruct?

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

Which costs less, GPT-5.6 Sol or Phi-4 Multimodal Instruct?

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 Sol or Phi-4 Multimodal Instruct?

A complete documented context-window comparison is not available.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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