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ModelCap

Phi 4

Current board #207Arena #306Official publisher releaseOpen weights7d #184 → #207

Microsoft·microsoft/phi-4

Phi 4 is ModelCap current-board rank #207 with Index 13.8 (Measured), from the ModelCap Index as of 22 Sept 2026, 02:49 UTC.

Phi 4 listed OpenRouter input price is $0.07 per 1M tokens and listed output price is $0.14 per 1M tokens, from OpenRouter listed prices as of 22 Sept 2026, 02:49 UTC.

Phi 4 has a published context limit of 16K tokens in the ModelCap catalogue as of 22 Sept 2026, 02:49 UTC.

Phi 4 has 1 published OpenRouter serving endpoint, from OpenRouter as of 22 Sept 2026, 02:49 UTC.

Phi 4 weight access is open weights, from ModelCap classification of published repository metadata as of 22 Sept 2026, 02:49 UTC.

Snapshot facts · download the public dataset · methodology

ModelCap Index · Current board
13.8#207
Measured · 63% support · score interval 8.8–18.8
Evidence as of 13 Sept 2026, 00:00 UTC
Market Gravity
19.3Decreased by 0.2
Secondary market signal
Input
$0.07
per 1M tokens
Output
$0.14
per 1M tokens
Context
16K
tokens
Providers
1
endpoint
Gateway spend
latest · Vercel share

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
DeepInfrabf16$0.07$0.1416Kbf16100.0%

Hugging Face Inference Providers

Exact repository identity

Provider economics and telemetry published by the Hugging Face Router. These are operational signals, not benchmark results.

ProviderInputOutputContextTTFTThroughputFeatures
deepinfra
live
$0.07$0.1416K345 ms27.2 tok/sstructured
featherless-ai
live

Reported evaluation leads (3)

Not independent measurement

Structured rows reported in this exact model repository at the pinned revision below. They are the publisher talking about itself: never measured evidence, never a score input on their own. They remain quarantined until dataset revision, harness, configuration and identity pass ModelCap’s independent admission review.

Dataset / taskMetricValueRevisionProvenance
LEXam-Benchmark/LEXam
open_question
38.54
Unpinned
not published
Reported
Quarantined
LEXam-Benchmark/LEXam
mcq_4_choices
40.66
Unpinned
not published
Reported
Quarantined
TIGER-Lab/MMLU-Pro
mmlu_pro
70.4
Unpinned
not published
Reported
Quarantined

ModelCap Index · Current board

Methodology
13.8
Measured · 2 public benchmark observations across 2 boards
Current board
#207
Public rank basis
Measured (measured)
Index support
62.8% · strong
Index interval
8.8–18.8
Rank posterior
Not available
Top 5 / Top 10 probability
Not available / Not available
Posterior as of
Snapshot timestamp unavailable
Evidence as of
13 Sept 2026, 00:00 UTC
Identity binding
Exact catalogue product · microsoft/microsoft/phi-4 · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata microsoft/phi-4@2db69c1c3e91a05d2c64a3185acfbaf36f744e25 (not the evaluation revision)
Index observations used
2
Qualified Index evidence point
13.8
Bayesian Evidence Score · measured evidence input only

BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.

Observed capability
13.8
Evidence status
confirmed · 77% mass
General preference
13.6
Coding
15.2
Agents & tools
Reasoning
Evidence breadth
92%
Evidence coverage
63%
Market and catalogue signals · secondary, never the language Index rank
Usage (OpenRouter popularity)
4.5
Liquidity (providers × uptime)
20.3
Open reach (HF downloads)
75.0
Surface (context / tools / modalities)
10.1
Freshness
9.4
  • · OpenRouter popularity #248
  • · 1 live provider
  • · HF 30d downloads 632759
  • · Catalogue: context 16384
  • · First seen 2025-01-10

These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.

BES evidence selected · measured input only

The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.

Deployment readiness

Metadata index
79.0
strong · 94% metadata coverage
Not capability
Artifact reproducibility
85.0
Access & legal clarity
100.0
Deployability
76.5
Evaluation provenance
52.5

Missing: artifactReproducibility.base-lineage, evaluationProvenance.evaluation-dataset-revisions.

A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.

Market activity

Venue coverage
Market Gravity
19.3
OpenRouter weekly popularity
#248 of 322

Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.

Arena preference

Status
Ranked
Source
LMArena
Official rank
#306 of 402
Rating
1256.1
95% confidence interval
1252–1261
Votes
24K
Source published at
13 Sept 2026, 00:00 UTC
Category
overall
Identity match confidence
100%

Market Gravity breakdown

Usage55%
4.5

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
20.3

Independent providers versus the model's open or closed cohort, weighted by uptime.

Open reach15%
75.0

Hugging Face 30-day downloads for open models; neutral for closed models.

Freshness5%
9.4

Time since first public availability, on a six-month half-life.

Specification

Context window
16K tokens
Max output
15K tokens
Inputs
Text
Outputs
Text
Tokenizer
Other
Cached input tokens
Not offered
First seen on OpenRouter
10 Jan 2025
Knowledge cutoff
2024-06-30

Capabilities

  • Not supported: Reasoning
  • Not supported: Tool use
  • Supported: Structured output
  • Supported: Response format
  • Not supported: Moderated

Weights & access

Open weights. Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI.

Access
Open weights
Licence
mit
Pinned revision
2db69c1c3e91
Parameters
14.7B
Architecture
Phi3ForCausalLM
Model type
phi3
Hugging Face downloads (30d)
633K
Downloads (all time)
14.4M
Likes
2K
Repository
microsoft/phi-4
Repository updated
14 Jul 2026, 14:22 UTC

Compare Phi 4 with

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Benchmark leaderboards featuring Phi 4

All leaderboards
Public benchmark boards on which Phi 4 has a published result
LeaderboardScoreSource rankConfigurationPublished
Arena coding leaderboardArena (LMArena)13061296–1316#292 of 397Default13 Sept 2026
BFCL V4 function-calling leaderboardUC Berkeley (Gorilla)28.8#52 of 83Prompt12 Apr 2026
τ²-Bench Telecom (Artificial Analysis run)Artificial Analysis0.0%#414 of 436tau2:telecom:dual-control:pass-at-1:3-repeats:source-model="Phi-4":reasoning=false9 Sept 2026
Humanity's Last Exam (Artificial Analysis run)Artificial Analysis3.8%#554 of 615hle:may-2025:text-only-2158:no-tools:pass-at-1:source-model="Phi-4":reasoning=false9 Sept 2026

Alternatives to Phi 4, by the numbers

Ranked neighbours

Models within 3 places of #207 on the ModelCap Index.

Cheaper at a similar or higher Index

Index score within 3 points of, or above, this model's, with a lower listed blended price (3:1 input:output).

Other ranked Microsoft models

The same publisher's other current models with a public Index position.

Phi 4: common questions

How does Phi 4 rank among AI models?

Phi 4 holds ModelCap Index position #207 of 248 ranked language models as of 22 September 2026, with an Index score of 13.8 (interval 8.8–18.8); evidence: measured. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.

How much does Phi 4 cost per 1M tokens?

Phi 4 is listed at $0.07 per 1M input tokens and $0.14 per 1M output tokens as of 22 September 2026. Prices come from the OpenRouter catalogue and re-observe on every refresh. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply $0.21 total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.

What is the context window of Phi 4?

Phi 4 has a published context window of 16K tokens (16,384) and a maximum output of 15K tokens. Individual providers can serve less than the published maximum; the providers table lists each endpoint's own limit.

Is Phi 4 open-weight?

Open weights (mit). Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI. The repository is microsoft/phi-4 on Hugging Face.

Which API providers serve Phi 4?

One provider serves Phi 4 through OpenRouter as of 22 September 2026: DeepInfra. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

Which benchmarks has Phi 4 been evaluated on?

Phi 4 has published results on 4 public boards tracked by ModelCap as of 22 September 2026: Arena coding 1306 (#292 of 397), BFCL V4 28.8 (#52 of 83), AA τ²-Bench Telecom 0.0% (#414 of 436) and AA Humanity's Last Exam 3.8% (#554 of 615). Each board page ranks every tracked model on that benchmark; the ModelCap Index combines them with published uncertainty.

How does Phi 4 compare with Olmo Hybrid 7B?

Phi 4 ranks #207 (Index 13.8) and Olmo Hybrid 7B ranks #208 (Index 13.7) on the ModelCap Index as of 22 September 2026. The head-to-head page lines up their benchmarks, listed prices, context windows and provider counts side by side.

When was Phi 4 released?

Phi 4 first appeared in the catalogue on 10 Jan 2025, with a published knowledge cutoff of 2024-06-30. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.