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ModelCap

Phi 4

Arena #323Official publisher releaseOpen weights7d #297 → #290

Microsoft·#290 of 352 on the current board·cheaper than #279 Command R+ (08-2024)

Phi 4 is a language model from Microsoft. It ranks #290 of 352 on ModelCap's current board, with an Index score of 18.6, measured on 2 public benchmarks. Its score range of 13.6–23.6 spans positions #268–#305. It is listed at $0.07 per million input tokens and $0.14 per million output tokens from one provider. It reads up to 16K tokens of context, and its weights are openly downloadable.

Figures as of 6 Nov 2026, 13:52 UTC · download the public dataset · methodology

ModelCap Index · Current board
18.6#290
Measured on 2 public benchmarks · range 13.6–23.6
Evidence as of 28 Oct 2026, 00:00 UTC
Market Gravity
18.8Increased by 0.1
Secondary market signal
Input
$0.07
per 1M tokens
Output
$0.14
per 1M tokens
Context
16K
tokens
Providers
1
2 endpoints

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
DeepInfrabf16/flexCheapest$0.056$0.11216Kbf16—
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$0.1416K527 ms109.3 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

Other versions (3)

ModelCap Index · Current board

Methodology
18.6
Measured on 2 public benchmarks
Current board
#290 of 352
Score range
13.6–23.6
Rank range
#268–#305
Evidence as of
28 Oct 2026, 00:00 UTC
Benchmarks used
Arena Coding, Arena Overall
Technical details
Public rank basis
Measured (measured)
Evidence label
Measured · 2 public benchmark observations across 2 boards
Index support
62.8% · strong
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
18.6
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
18.6
Evidence status
confirmed · 77% mass
General preference
18.1
Coding
22.1
Agents & tools
—
Reasoning
—
Evidence breadth
92%
Evidence coverage
63%
Market and catalogue signals · secondary, never the language Index rank
Usage (OpenRouter popularity)
5.8
Liquidity (providers × uptime)
24.3
Open reach (HF downloads)
60.7
Surface (context / tools / modalities)
10.1
Freshness
7.9
  • · OpenRouter popularity #242
  • · 1 live provider
  • · HF 30d downloads 299090
  • · 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
73.0
partial · 88% metadata coverage
Not capability
Artifact reproducibility
85.0
Access & legal clarity
100.0
Deployability
56.5
Evaluation provenance
52.5

Not yet published: the base model it derives from, measured provider uptime, pinned evaluation dataset versions.

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

Market activity

Venue coverage
Market Gravity
18.8
OpenRouter weekly popularity
#242 of 339

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
#323 of 419
Rating
1256.3
95% confidence interval
1252–1261
Votes
24K
Source published at
28 Oct 2026, 00:00 UTC
Category
overall
Identity match confidence
100%

Market Gravity breakdown

Usage55%
5.8

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
24.3

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

Open reach15%
60.7

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

Freshness5%
7.9

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

Specification

Model ID
microsoft/phi-4
Context window
16K tokens
Declared output cap
15K tokens

OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. OpenRouter metadata

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)
299K
Downloads (all time)
14.9M
Likes
2K
Repository
microsoft/phi-4
Repository updated
14 Jul 2026, 14:22 UTC

Compare Phi 4 with

Pick any model

Benchmark leaderboards featuring Phi 4

All leaderboards
Public benchmark boards on which Phi 4 has a published result
LeaderboardScoreSource rankConfigurationPublished
Arena text leaderboardArena (LMArena)12561252–1261#323 of 419Not reported28 Oct 2026
Arena coding leaderboardArena (LMArena)13061296–1316#310 of 414Default28 Oct 2026
BFCL V4 function-calling leaderboardUC Berkeley (Gorilla)28.8#52 of 83Prompt12 Apr 2026

Alternatives to Phi 4, by the numbers

Ranked neighbours

Models within 3 places of #290 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 #290 of 352 ranked language models as of 6 November 2026, with an Index score of 18.6 (interval 13.6–23.6); 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 6 November 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). The snapshot lists a 14,745 token catalogue-declared route output cap. OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. The providers table lists each endpoint's context 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 6 November 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 3 public boards tracked by ModelCap as of 6 November 2026: Arena text 1256 (#323 of 419), Arena coding 1306 (#310 of 414) and BFCL V4 28.8 (#52 of 83). Each board page ranks every tracked model on that benchmark. The ModelCap Index scores the results its methodology admits, with published uncertainty, and shows reference boards without scoring them.

How does Phi 4 compare with Phi 3 medium 4k instruct?

Phi 4 ranks #290 (Index 18.6) and Phi 3 medium 4k instruct ranks #319 (Index 11.8) on the ModelCap Index as of 6 November 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.