Phi-4 vs Qwen3.8-Flash-Next

Updated October 10, 2026. Rank says Qwen3.8-Flash-Next is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.8-Flash-Next has the higher public point estimate, 64.23 versus 25.22. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 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

Phi-4

Microsoft

25.22/100

Supported · Public rank #198

90% interval 13.6–36.8

Model B

Qwen3.8-Flash-Next

Alibaba

64.23/100

Estimated · Public rank #37

Conditional range 49.9–78.6

Shared results: 1
Phi-4 only: 4
Qwen3.8-Flash-Next only: 23
Like-for-like categories: 1 / 8

Supported: Phi-4 · Estimated: Qwen3.8-Flash-Next. Conditional ranges do not establish rank confidence.How 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
Qwen3.8-Flash-Next
Qwen3.8-Flash-Next 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 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 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 does not fit this workload in one request. Phi-4 has no comparable published API token rate. Qwen3.8-Flash-Next 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 does not fit this workload in one request. Phi-4 has no comparable published API token rate. Qwen3.8-Flash-Next 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.

—Phi-452.4Qwen3.8-Flash-Next

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 35.6
Phi-4:56.1%
Qwen3.8-Flash-Next:91.7%
Phi-4 source Qwen3.8-Flash-Next 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.

Knowledge

Like-for-like

Phi-4: 23.5; Supported · #168/177
Qwen3.8-Flash-Next: 59.4; Supported · #47/177
Basis: BenchAlign v5.8 lane · 2 vs 4 public rows
Reading: Qwen3.8-Flash-Next leads

Instruction following

Directional only

Phi-4: 28.8; #117/127
Qwen3.8-Flash-Next: 91.2; #22/127
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Agentic

Not comparable

Phi-4: Not ranked
Qwen3.8-Flash-Next: 59.5; Estimated · #24/123
Basis: BenchAlign v5.8 lane · 0 vs 6 public rows
Reading: Not comparable

Coding

Not comparable

Phi-4: Not ranked
Qwen3.8-Flash-Next: 52.4; Supported · #37/146
Basis: BenchAlign v5.8 lane · 1 vs 5 public rows
Reading: Not comparable

Reasoning

Not comparable

Phi-4: 25.9; Unranked · 2 rankable rows
Qwen3.8-Flash-Next: 80.9; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Phi-4: Not ranked
Qwen3.8-Flash-Next: 87.7; #8/54
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Phi-4: 1.0; #17/17
Qwen3.8-Flash-Next: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Phi-4: Not ranked
Qwen3.8-Flash-Next: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Phi-4 and Qwen3.8-Flash-Next
Category	Phi-4	Qwen3.8-Flash-Next	Basis	Reading
Knowledge	23.5Supported · #168/177	59.4Supported · #47/177	Like-for-likeBenchAlign v5.8 lane · 2 vs 4 public rows	Qwen3.8-Flash-Next leads
Instruction following	28.8#117/127	91.2#22/127	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Agentic	Not ranked	59.5Estimated · #24/123	Not comparableBenchAlign v5.8 lane · 0 vs 6 public rows	Not comparable
Coding	Not ranked	52.4Supported · #37/146	Not comparableBenchAlign v5.8 lane · 1 vs 5 public rows	Not comparable
Reasoning	25.9Unranked · 2 rankable rows	80.9Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	87.7#8/54	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multilingual	1.0#17/17	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 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

Phi-4: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request

Phi-4 has no comparable published API token rate. Qwen3.8-Flash-Next has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Phi-4: Self-hosted; infrastructure cost varies; Does not fit in one request
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request

Phi-4 does not fit this workload in one request. Phi-4 has no comparable published API token rate. Qwen3.8-Flash-Next has no comparable published API token rate.

Cache-heavy agent loop

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

Phi-4: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Phi-4 does not fit this workload in one request. Phi-4 has no comparable published API token rate. Qwen3.8-Flash-Next 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.

SpecificationPhi-4Qwen3.8-Flash-Next

Context window

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

Phi-4

16K

Qwen3.8-Flash-Next

262K

Qwen3.8-Flash-Next model card

API model ID

Phi-4

Not sourced

Qwen3.8-Flash-Next

Qwen/Qwen3.8-Flash-Next

Qwen3.8-Flash-Next model card

Cached-input rate

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

Phi-4

No comparable hosted API rate

Qwen3.8-Flash-Next

No comparable hosted API rate

Qwen3.8-Flash-Next model card

Documented inputs

Phi-4

Not sourced

Qwen3.8-Flash-Next

Not sourced

Documented outputs

Phi-4

Not sourced

Qwen3.8-Flash-Next

Not sourced

Provider availability

Phi-4

Not sourced

Qwen3.8-Flash-Next

Not sourced

Reasoning profile

Phi-4

Non-Reasoning

Qwen3.8-Flash-Next

Reasoning

Weight access

Phi-4

Open Weight

Qwen3.8-Flash-Next

Open Weight

License

Phi-4

Open Weight

Qwen3.8-Flash-Next

Open Weight

Release date

Phi-4

2025-01-01

Qwen3.8-Flash-Next

2026-08-26

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: Qwen3.8-Flash-Next has the higher public point estimate, 64.23 versus 25.22. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.8-Flash-Next has the larger documented window (262K).

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

Questions

Which is better, Phi-4 or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next has the higher public point estimate, 64.23 versus 25.22. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Phi-4 or Qwen3.8-Flash-Next?

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

Which is better for agentic tasks, Phi-4 or Qwen3.8-Flash-Next?

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

Which costs less, Phi-4 or Qwen3.8-Flash-Next?

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, Phi-4 or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next has the larger documented context window: 262K, compared with 16K.

Benchmark evidence

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

Browse raw public benchmark evidence28 rows

Agentic

CoWorkBench
Phi-4—
Qwen3.8-Flash-Next73.9%
Source
Not directly comparable
JobBench
Phi-4—
Qwen3.8-Flash-Next55.7%
Source
Not directly comparable
Agents' Last Exam
Phi-4—
Qwen3.8-Flash-Next51.2%
Source
Not directly comparable
Toolathlon-Verified
Phi-4—
Qwen3.8-Flash-Next73.5%
Source
Not directly comparable
AndroidWorld
Phi-4—
Qwen3.8-Flash-Next84.5%
Source
Not directly comparable
OSWorld 2.0
Phi-4—
Qwen3.8-Flash-Next19.4%
Source
Not directly comparable

Coding

HumanEval
Phi-482.6%
Source
Qwen3.8-Flash-Next—
Not directly comparable
SWE-bench Pro
Phi-4—
Qwen3.8-Flash-Next62.5%
Source
Not directly comparable
SWE Multilingual
Phi-4—
Qwen3.8-Flash-Next81%
Source
Not directly comparable
NL2Repo
Phi-4—
Qwen3.8-Flash-Next48.1%
Source
Not directly comparable
DeepSWE
Phi-4—
Qwen3.8-Flash-Next58.7%
Source
Not directly comparable
LiveCodeBench v6
Phi-4—
Qwen3.8-Flash-Next91.9%
Source
Not directly comparable

Multimodal

Vision2Web
Phi-4—
Qwen3.8-Flash-Next64.0%
Source
Not directly comparable
ERQA
Phi-4—
Qwen3.8-Flash-Next72.3%
Source
Not directly comparable
LVBench
Phi-4—
Qwen3.8-Flash-Next76.6%
Source
Not directly comparable
RealWorldQA
Phi-4—
Qwen3.8-Flash-Next88.5%
Source
Not directly comparable
MathVision
Phi-4—
Qwen3.8-Flash-Next90.6%
Source
Not directly comparable
MathVision w/ Python
Phi-4—
Qwen3.8-Flash-Next95.7%
Source
Not directly comparable
CharXiv w/o tools
Phi-4—
Qwen3.8-Flash-Next84.6%
Source
Not directly comparable
CharXiv
Phi-4—
Qwen3.8-Flash-Next90.6%
Source
Not directly comparable

Knowledge

MMLU
Phi-484.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
GPQA
Phi-456.1%
Source
Qwen3.8-Flash-Next91.7%
Source
Qwen3.8-Flash-Next leads this result
GPQA-D
Phi-4—
Qwen3.8-Flash-Next91.7%
Source
Not directly comparable
HLE
Phi-4—
Qwen3.8-Flash-Next35.9%
Source
Not directly comparable
HLE w/o tools
Phi-4—
Qwen3.8-Flash-Next35.9%
Source
Not directly comparable

Multilingual

MGSM
Phi-480.6%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MMLU-ProX
Phi-449.9%
Source
Qwen3.8-Flash-Next—
Not directly comparable

Instruction following

IFBench
Phi-4—
Qwen3.8-Flash-Next81.3%
Source
Not directly comparable

28 public results · 1 shared

Related comparisons

Compare API pricing Read the methodology Open the model selector

Watch Phi-4 vs Qwen3.8-Flash-Next

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 5,500+ readers.

Last updated October 10, 2026

Phi-4 vs Qwen3.8-Flash-Next

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

Knowledge

Instruction following

Agentic

Coding

Reasoning

Multimodal

Multilingual

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, Phi-4 or Qwen3.8-Flash-Next?

Which is better for coding, Phi-4 or Qwen3.8-Flash-Next?

Which is better for agentic tasks, Phi-4 or Qwen3.8-Flash-Next?

Which costs less, Phi-4 or Qwen3.8-Flash-Next?

Which has the larger context window, Phi-4 or Qwen3.8-Flash-Next?

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Multilingual

Instruction following

Related comparisons

Watch Phi-4 vs Qwen3.8-Flash-Next