SSyncDev

AI model comparison

Llama 3.1 405B vs Phi 4

Compare recorded price, context, output limits, speed, latency, capabilities, and input/output support. Every missing value stays visible, and unsourced benchmarks are excluded.

This Llama 3.1 405B vs Phi 4 comparison is designed for teams choosing between two production model records, not for declaring a universal winner. Llama 3.1 405B by Meta currently records 128K context, 4.1K maximum output, $3.50 input per 1M tokens, $3.50 output per 1M tokens, 30 t/s recorded output speed, 0.70s recorded time to first token. Phi 4 by Deepinfra via Requesty records 16.4K context, $0.07 input per 1M tokens, $0.14 output per 1M tokens. The tables below separate documented capabilities, commercial limits, directory measurements, and source-linked benchmark evidence so you can see where the data is complete and where independent testing is still required.

Side-by-sideSpecs and pricing, compared

Meta

Llama 3.1 405B

Meta's largest open-weights model, competitive with frontier closed models.

Context
128K
Max output
4.1K
Input / 1M
$3.50
Output / 1M
$3.50
Output speed
30 t/s

Accepts

  • Text input

Produces

  • Text output
Full model profileVendor docs ↗
VS

Deepinfra via Requesty

Phi 4

Phi-4-reasoning-plus is an enhanced 14B parameter model from Microsoft, fine-tuned from Phi-4 with additional reinforcement learning to boost accuracy on math, science, and code reasoning tasks. It uses the same dense decoder-only transformer architecture as Phi-4, but generates longer, more comprehensive outputs structured into a step-by-step reasoning trace and final answer. While it offers improved benchmark scores over Phi-4-reasoning across tasks like AIME, OmniMath, and HumanEvalPlus, its responses are typically ~50% longer, resulting in higher latency. Designed for English-only applications, it is well-suited for structured reasoning workflows where output quality takes priority over response speed.

Context
16.4K
Max output
—
Input / 1M
$0.07
Output / 1M
$0.14
Output speed
—

Accepts

  • Text input

Produces

  • Text output
Full model profileVendor docs ↗

At a glance

Decision snapshot

This is a directional summary of the values recorded in this directory—not a universal quality verdict. Test the finalists on your own prompts before committing.

Where Llama 3.1 405B leads in recorded data

  • Context capacity: 128K

Where Phi 4 leads in recorded data

  • Input price: $0.07
  • Output price: $0.14

Context capacity

Higher listed limit

Llama 3.1 405B:128K

Phi 4:16.4K

Llama 3.1 405B

Maximum output

Higher listed limit

Llama 3.1 405B:4.1K

Phi 4:—

No complete comparison

Input price

Lower recorded price

Llama 3.1 405B:$3.50

Phi 4:$0.07

Phi 4

Output price

Lower recorded price

Llama 3.1 405B:$3.50

Phi 4:$0.14

Phi 4

Output speed

Higher recorded throughput

Llama 3.1 405B:30 t/s

Phi 4:—

No complete comparison

Time to first token

Lower recorded latency

Llama 3.1 405B:0.70s

Phi 4:—

No complete comparison

Intelligence Index

Higher directory index

Llama 3.1 405B:74.0

Phi 4:—

No complete comparison

Evidence

Verified shared benchmarks

A score appears only when both records include a verified source. It is not treated as a universal ranking.

These two models haven't been measured on the same benchmark yet, so there's nothing meaningful to chart. Comparing scores from different tests would tell you more about the tests than the models — the specs and pricing above are the better guide here.

Specifications

Pricing, limits and capabilities

SpecificationLlama 3.1 405BPhi 4
ProviderMetaDeepinfra via Requesty
Model familyLlama 3.1—
Accepted inputstexttext
Produced outputstexttext
Context window128KRecorded lead16.4K
Max output4.1K—
Intelligence Index74.0—
Input price / 1M$3.50$0.07Recorded lead
Output price / 1M$3.50$0.14Recorded lead
Output speed30 t/s—
Time to first token0.70s—
Reasoning modeNot documentedNot documented
Tool callingDocumentedNot documented
Image inputNot documentedNot documented
Audio inputNot documentedNot documented
Structured outputDocumentedDocumented
LicenseOpen weightsNot documented
ReleasedJul 23, 2024Not documented

Workload guidance

Which model should you choose?

Llama 3.1 405B currently leads on larger context. Phi 4 currently leads on lower recorded input price, lower recorded output price. Choose according to the constraints that matter to your workload, then run the same representative prompts against both models before making a production commitment. Neither the larger number nor the lower price is automatically the better choice: prompt quality, tool reliability, modality support, rate limits, data policy, and provider implementation can change the result. Use this page to build a shortlist, confirm current terms in the linked sources, and validate quality, latency, and cost with your own traffic.

Provenance

Sources and data limits

Prices and hosted performance can change. SyncDev records source references and clearly separates documented specifications from measured values; confirm commercial terms with the provider before deployment.

Questions people ask

It compares recorded context and output limits, token prices, output speed, time to first token, Intelligence Index, modality support, tool and structured-output capabilities, release information, and only source-linked benchmarks verified on both records. Missing information is labeled rather than inferred.

Compare a different pair

Choose any two approved model records and review them with the same evidence rules.

Open model comparison