Undominated.ai

Model comparison · accepted catalogue facts 2026-10-06

DeepSeek V4 Flash 0423 vs gpt-oss-120b

Neither model has both an equal-or-higher measured score and an equal-or-lower estimated price in this pair. Retaining your current model is a valid outcome.

These models are adjacent on the default capability-price frontier. This is a direct pair comparison; global frontier membership is not its verdict.

DeepSeek V4 Flash 0423

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1432.1

Balanced effective USD / 1M tokens · lower is better

$0.113/M

gpt-oss-120b

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1365.4

Balanced effective USD / 1M tokens · lower is better

$0.065/M

Compare · model decision

Choose two or three models

Inspect the differences, estimate your workload and keep the evidence. All standard catalogue models are available, including unrated ones.

Catalogue facts
2026-10-06

2 / 3 selected · 0 requirements
Open public comparison link
Public workload and requirements

These settings are included in the share link. The fixed-request scenario and private evaluation data below are excluded.

A ↔ B

Neither model has both an equal-or-higher measured score and an equal-or-lower estimated price in this pair. Retaining your current model is a valid outcome.

Estimated blend: 75% input, 25% output; 0% cached input. Base context tier. Prompt length selects the billing tier. An unlisted cache-read rate uses that tier’s full input rate in this blend; missing required input or output rates prevent an estimate. The fixed-request estimate below validates actual context and output limits and requires a published cache-read rate when cache hits are assumed.

What changes between these models

Quality first, then estimated price. LMArena scores are human-preference measurements, not a benchmark of your application. A missing score is unrated.

  • DeepSeek V4 Flash 0423 · Meets the stated model requirements
  • gpt-oss-120b · Meets the stated model requirements
Material differences and unknown evidence. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA DeepSeek V4 Flash 0423B gpt-oss-120b
LMArena general score (Elo)1,432.11,365.4Δ vs A: -66.7 Elo
Score interval half-width (± Elo)4.034.37Δ vs A: +0.34 Elo
LMArena document score (Elo)UnknownUnknown
Estimated effective USD / 1M tokens$0.1125$0.065Δ vs A: -0.0475 USD/M
Base input USD / 1M tokens$0.09$0.03Δ vs A: -0.06 USD/M
Base output USD / 1M tokens$0.18$0.17Δ vs A: -0.01 USD/M
Base cached-input USD / 1M tokens$0.018$0.03Δ vs A: +0.012 USD/M
Context window (tokens)1,048,576131,072Δ vs A: -917,504 tokens
Output limit (tokens)943,718117,964Δ vs A: -825,754 tokens
Recorded licenceUnknownapache-2.0
Model providerDeepSeekOpenAI
Distinct sellers in the catalogue1620Δ vs A: +4
Recorded retirement dateUnknownUnknown
7 shared measured facts
Shared measured facts. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA DeepSeek V4 Flash 0423B gpt-oss-120b
Benchmark reasoning effortdefaultdefault
Input modalitiestexttext
Tool useSupportedSupportedΔ vs A: 0
ReasoningSupportedSupportedΔ vs A: 0
Open weightsSupportedSupportedΔ vs A: 0
Serving precision differs across offersSupportedSupportedΔ vs A: 0
Retirement evidenceNone announced; not a guaranteeNone announced; not a guarantee

Provider counts and precision flags describe catalogue offers. They do not establish the precision, availability, region, tool behaviour or limits of your chosen endpoint. Open each model’s provider table before switching.

Complete price ladders and billing conditions

DeepSeek V4 Flash 0423

Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token thresholdInputOutputCached input
Base, up to and including first threshold$0.09$0.18$0.018

gpt-oss-120b

Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token thresholdInputOutputCached input
Base, up to and including first threshold$0.03$0.17$0.03
Migration preflight · compare each candidate with model A

Keeping DeepSeek V4 Flash 0423 is a valid decision. No pairwise comparison certifies a drop-in replacement.

DeepSeek V4 Flash 0423 → gpt-oss-120b

Known loss or failed requirement

  • Context window: 1,048,576 → 131,072 tokens.
  • Output limit: 943,718 → 117,964 tokens.
  • Provider endpoint, serving precision, availability and task success need your own verification. Catalogue model capabilities do not certify an endpoint.
Check alternatives to DeepSeek V4 Flash 0423

Estimate a fixed request shape

Private to this tab unless you save locally or explicitly export private details. These are estimates, not observed invoices. Cross-model token counts and task success are assumptions.

0.1 extra attempts means 10 extra full attempts per 100 requests. Cache writes, tool charges, taxes and unlisted charges are excluded.

  • DeepSeek V4 Flash 0423Estimated total: $0.27 1,000 full attempts · base rates
  • gpt-oss-120bEstimated total: $0.145 1,000 full attempts · base rates
Prompt and output sensitivity · sampled scenarios

Every point recalculates the entire request shape. Tier-boundary samples include one token below, at and above each threshold. Ordering between samples is not guaranteed. Lowest known cost excludes unknown estimates and is not a replacement recommendation.

Vary input; hold output, requests, cache and retries fixed.
Input tokensDeepSeek V4 Flash 0423gpt-oss-120bCoverage
2,000$0.27$0.145Lowest knownAll computed
4,096$0.45864$0.20788Lowest knownAll computed
32,768$3.03912$1.06804Lowest knownAll computed
131,072$11.88648Lowest knownineligibleInput plus output exceeds the published context window.Incomplete
Vary output; hold input, requests, cache and retries fixed.
Output tokensDeepSeek V4 Flash 0423gpt-oss-120b
128$0.20304$0.08176
2,048$0.54864$0.40816
8,192$1.65456$1.45264

Bring your observed evaluation results

JSON is parsed locally. Prompts, filenames, individual responses and extra fields are not retained. Import only aggregate measurements for selected models. Task/version labels do not establish that two evaluations used the same dataset.

Expected format
[{"model":"provider/exact-model-id","task":"support-answer","version":"rubric-1","attempts":100,"successes":84,"costUsd":2.4,"latencyMs":950}]

Use the exact catalogue ID. Replace the example values with observed measurements. Omit costUsd or latencyMs when they are unknown. No public score is inferred from these observations.

Keep the decision and its evidence

A portable record retains selected facts, complete tier ladders, public requirements and the publication manifest. It also embeds the full permitted public catalogue to verify the input artifact checksum. Files are larger for this reason (up to 2 MB); local storage is limited to 12 records. Team review is local to this browser; there is no hosted collaboration.

Saving is opt-in and includes private review fields. Shared comparison links never include them. Version 1 Check references remain separate and are not overwritten or guessed into this format.

Saved local decisions · 0

No version 2 decision records are stored in this browser.

Questions this page answers

Is DeepSeek V4 Flash 0423 a better-and-cheaper replacement for gpt-oss-120b?

Neither model has both an equal-or-higher measured score and an equal-or-lower estimated price in this pair. Retaining your current model is a valid outcome.

What is included in this comparison?

Accepted model capabilities, context and output limits, complete price-tier ladders, independent LMArena measurements and missing evidence. Provider endpoint precision and application success still need verification.

Can I compare a different workload or keep a decision record?

Yes. Change the public workload, prompt length and requirements in the comparison workspace. Fixed-request scenarios, aggregate evaluations and review notes stay local unless explicitly exported.

Evidence & Ask
Markdown for LLMs