Coding
Directional only- GPT-4o mini
- 31.6
- Estimated · #138/154
- Qwen3.8-Omni-Flash
- 53.5
- Estimated · #45/154
- Basis
- BenchAlign lane · 0 vs 5 public rows
- Reading
- Directional only
GPT-4o mini vs Qwen3.8-Omni-Flash
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 18, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
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-Omni-Flash
Qwen3.8-Omni-Flash 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
GPT-4o mini and Qwen3.8-Omni-Flash are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Qwen3.8-Omni-Flash 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. GPT-4o mini does not fit this workload in one request. GPT-4o mini has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Omni-Flash has no comparable published API token rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
19 Qwen3.8-Omni-Flash only
- Shared results
- 0
- GPT-4o mini only
- 0
- Qwen3.8-Omni-Flash only
- 19
- Like-for-like categories
- 0 / 8
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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
Directional only- GPT-4o mini
- 30.9
- Estimated · #177/184
- Qwen3.8-Omni-Flash
- 54.9
- Estimated · #54/184
- Basis
- BenchAlign lane · 0 vs 3 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-4o mini
- 33.2
- #113/124
- Qwen3.8-Omni-Flash
- 87.7
- #29/124
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-4o mini
- 33.3
- Estimated · #140/154
- Qwen3.8-Omni-Flash
- Not ranked
- Basis
- BenchAlign lane · 0 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4o mini
- Not ranked
- Qwen3.8-Omni-Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-4o mini
- Not ranked
- Qwen3.8-Omni-Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4o mini
- Not ranked
- Qwen3.8-Omni-Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4o mini
- 25.4
- Unranked · 1 rankable row
- Qwen3.8-Omni-Flash
- 85.1
- Unranked · 7 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | GPT-4o mini | Qwen3.8-Omni-Flash | Basis | Reading |
|---|---|---|---|---|
| Coding | 31.6Estimated · #138/154 | 53.5Estimated · #45/154 | Directional onlyBenchAlign lane · 0 vs 5 public rows | Directional only |
| Knowledge | 30.9Estimated · #177/184 | 54.9Estimated · #54/184 | Directional onlyBenchAlign lane · 0 vs 3 public rows | Directional only |
| Instruction following | 33.2#113/124 | 87.7#29/124 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Agentic | 33.3Estimated · #140/154 | Not ranked | Not comparableBenchAlign lane · 0 vs 2 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 25.4Unranked · 1 rankable row | 85.1Unranked · 7 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
- GPT-4o mini
- $0.00045
- Fits in one request
- Qwen3.8-Omni-Flash
- API rate not published
- Fits in one request
Qwen3.8-Omni-Flash has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-4o mini
- $0.0093
- Fits in one request
- Qwen3.8-Omni-Flash
- API rate not published
- Fits in one request
Qwen3.8-Omni-Flash has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-4o mini
- $0.039
- Does not fit in one request
- Cached input priced at the published list-input rate
- Qwen3.8-Omni-Flash
- API rate not published
- Fits in one request
- Cached-input rate unavailable
GPT-4o mini does not fit this workload in one request. GPT-4o mini has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Omni-Flash has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-4o miniQwen3.8-Omni-Flash
Context window
Maximum documented context; output-token limits may be lower.
GPT-4o mini
128K
Qwen3.8-Omni-Flash
API model ID
GPT-4o mini
Not sourced
Qwen3.8-Omni-Flash
qwen3.8-omni-flash
Alibaba Cloud Model Studio pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-4o mini
Not published
Qwen3.8-Omni-Flash
No comparable hosted API rate
Alibaba Cloud Model Studio pricingDocumented inputs
GPT-4o mini
Not sourced
Qwen3.8-Omni-Flash
Not sourced
Documented outputs
GPT-4o mini
Not sourced
Qwen3.8-Omni-Flash
Not sourced
Provider availability
GPT-4o mini
Not sourced
Qwen3.8-Omni-Flash
Not sourced
Reasoning profile
GPT-4o mini
Non-Reasoning
Qwen3.8-Omni-Flash
Reasoning
Weight access
GPT-4o mini
Proprietary
Qwen3.8-Omni-Flash
Proprietary
License
GPT-4o mini
Proprietary
Qwen3.8-Omni-Flash
Proprietary
Release date
GPT-4o mini
2024-07-18
Qwen3.8-Omni-Flash
2026-09-18
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Qwen3.8-Omni-Flash has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence19 rows
Agentic
Coding
SWE-bench Pro
GPT-4o mini—Qwen3.8-Omni-Flash63.3%SourceNot directly comparable
SWE Multilingual
GPT-4o mini—Qwen3.8-Omni-Flash80.5%SourceNot directly comparable
NL2Repo
GPT-4o mini—Qwen3.8-Omni-Flash48.9%SourceNot directly comparable
DeepSWE
GPT-4o mini—Qwen3.8-Omni-Flash57.8%SourceNot directly comparable
LiveCodeBench v6
GPT-4o mini—Qwen3.8-Omni-Flash92.6%SourceNot directly comparable
Knowledge
Multimodal
Vision2Web
GPT-4o mini—Qwen3.8-Omni-Flash62.9%SourceNot directly comparable
ERQA
GPT-4o mini—Qwen3.8-Omni-Flash71.0%SourceNot directly comparable
LVBench
GPT-4o mini—Qwen3.8-Omni-Flash76.9%SourceNot directly comparable
RealWorldQA
GPT-4o mini—Qwen3.8-Omni-Flash87.7%SourceNot directly comparable
MathVision
GPT-4o mini—Qwen3.8-Omni-Flash91.8%SourceNot directly comparable
MathVision w/ Python
GPT-4o mini—Qwen3.8-Omni-Flash96.2%SourceNot directly comparable
CharXiv w/o tools
GPT-4o mini—Qwen3.8-Omni-Flash83.5%SourceNot directly comparable
CharXiv
GPT-4o mini—Qwen3.8-Omni-Flash91.4%SourceNot directly comparable
Instruction following
IFBench
GPT-4o mini—Qwen3.8-Omni-Flash81.5%SourceNot directly comparable
Questions
Which is better, GPT-4o mini or Qwen3.8-Omni-Flash?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-4o mini or Qwen3.8-Omni-Flash?
Qwen3.8-Omni-Flash scores higher for coding on the public lane, 53.5 to 31.6. GPT-4o mini and Qwen3.8-Omni-Flash are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-4o mini or Qwen3.8-Omni-Flash?
Qwen3.8-Omni-Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-4o mini or Qwen3.8-Omni-Flash?
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, GPT-4o mini or Qwen3.8-Omni-Flash?
Qwen3.8-Omni-Flash has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated September 18, 2026
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