Math
Directional only- GPT-4o
- 0.3
- Qwen3.5 Flash
- 4.7
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
GPT-4o vs Qwen3.5 Flash
Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Qwen3.5 Flash has the higher public score estimate, 47.11 versus 40.66, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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
Qwen3.5 Flash
Qwen3.5 Flash has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Qwen3.5 Flash
Qwen3.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Qwen3.5 Flash
Qwen3.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
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 does not fit this workload in one request. GPT-4o has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 Shared
1 Qwen3.5 Flash only
- Shared results
- 1
- GPT-4o only
- 0
- Qwen3.5 Flash only
- 1
- Like-for-like categories
- 0 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Agentic
Not comparable- GPT-4o
- Not measured
- Qwen3.5 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- GPT-4o
- Not measured
- Qwen3.5 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4o
- Not measured
- Qwen3.5 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-4o
- Not measured
- Qwen3.5 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4o
- Not measured
- Qwen3.5 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4o
- Not measured
- Qwen3.5 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-4o
- Not measured
- Qwen3.5 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-4o | Qwen3.5 Flash | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 0.3 | 4.7 | Directional only1 vs 2 rows | Directional only |
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
FrontierMath v2 (Tiers 1-3)
Math
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
- $0.0075
- Fits in one request
- Qwen3.5 Flash
- $0.0003
- Fits in one request
Qwen3.5 Flash has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-4o
- $0.155
- Fits in one request
- Qwen3.5 Flash
- $0.0062
- Fits in one request
Qwen3.5 Flash has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-4o
- $0.65
- Does not fit in one request
- Cached input priced at the published list-input rate
- Qwen3.5 Flash
- $0.026
- Fits in one request
- Cached input priced at the published list-input rate
GPT-4o does not fit this workload in one request. GPT-4o has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-4oQwen3.5 Flash
Context window
Maximum documented context; output-token limits may be lower.
GPT-4o
128K
Qwen3.5 Flash
1M
API model ID
GPT-4o
Not sourced
Qwen3.5 Flash
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-4o
Not published
Qwen3.5 Flash
Not published
Documented inputs
GPT-4o
Not sourced
Qwen3.5 Flash
Not sourced
Documented outputs
GPT-4o
Not sourced
Qwen3.5 Flash
Not sourced
Provider availability
GPT-4o
Not sourced
Qwen3.5 Flash
Not sourced
Reasoning profile
GPT-4o
Non-Reasoning
Qwen3.5 Flash
Reasoning
Weight access
GPT-4o
Proprietary
Qwen3.5 Flash
Proprietary
License
GPT-4o
Proprietary
Qwen3.5 Flash
Proprietary
Release date
GPT-4o
2024-05-13
Qwen3.5 Flash
2026-03-04
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
- Qwen3.5 Flash has the higher public score estimate, 47.11 versus 40.66, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.155 vs $0.0062. Cache-heavy agent loop: $0.65 vs $0.026.
- Context tradeoff
- Qwen3.5 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 evidence2 rows
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-4o0.345%Qwen3.5 Flash6.207%Qwen3.5 Flash leads this result
FrontierMath v2 (Tier 4)
GPT-4o—Qwen3.5 Flash0.000%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-4o or Qwen3.5 Flash?
Qwen3.5 Flash has the higher public score estimate, 47.11 versus 40.66, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-4o or Qwen3.5 Flash?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-4o or Qwen3.5 Flash?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GPT-4o or Qwen3.5 Flash?
For the stated presets, chat costs $0.0075 on GPT-4o and $0.0003 on Qwen3.5 Flash; repository review costs $0.155 and $0.0062; the cache-heavy agent loop costs $0.65 and $0.026. GPT-4o does not fit this workload in one request. GPT-4o has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-4o or Qwen3.5 Flash?
Qwen3.5 Flash has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated August 13, 2026
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