DeepSeek V4 Flash Vision Exp vs Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads DeepSeek V4 Flash Vision Exp on the Artificial Analysis Intelligence Index, 39.9 to 34.8, and DeepSeek V4 Flash Vision Exp costs 4.5x less per output token ($1.32 vs $6.00 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
DeepSeek V4 Flash Vision Exp or Qwen3.8 2.4T A95B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V4 Flash Vision Exp for output tokens 4.5x cheaper ($1.32 vs $6.00 per million) and work measured by AA Coding Index or SciCode, where it leads.
- Shared results
- 7
- DeepSeek V4 Flash Vision Exp only
- 7
- Qwen3.8 2.4T A95B only
- 2
- Like-for-like categories
- 5 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads, 39.9 against 34.8.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads, 50.1 against 47.5.
Same board, both measured
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Flash Vision Exp
DeepSeek V4 Flash Vision Exp $0.0011, Qwen3.8 2.4T A95B $0.0050 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4 Flash Vision Exp not benchmarked, Qwen3.8 2.4T A95B not benchmarked.
- Widest category
- Coding, 5.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- Humanity's Last Exam (Artificial Analysis run): DeepSeek V4 Flash Vision Exp 34.5% against Qwen3.8 2.4T A95B 42.4%, 7.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash Vision Exp $1.32 against Qwen3.8 2.4T A95B $6.00 per million output tokens, 4.5x apart.
- Context window
- Both take 1.05M tokens.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V4 Flash Vision Exp | Bars | Qwen3.8 2.4T A95B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 1 | 0 | ||
| All shared boards | 1 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | DeepSeek V4 Flash Vision Exp | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| LiveBench coding | 37.6% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| AA Coding Index | 65.0 | 71.9 | Qwen3.8 2.4T A95B |
| SciCode | 49.7% | 54.1% | Qwen3.8 2.4T A95B |
| Neither model has a result on the other 40 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Flash Vision Exp | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Intelligence Index | 34.8 | 39.9 | Qwen3.8 2.4T A95B |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Flash Vision Exp | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Agentic Index | 47.5 | 50.1 | Qwen3.8 2.4T A95B |
| LiveBench research | 69.2% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| LiveBench browser | 64.0% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| LiveBench long agentic | 39.0% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| MCP Atlas | Not measured | 84.5% | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V4 Flash Vision Exp | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| LiveBench reasoning | 85.4% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| GPQA Diamond (Artificial Analysis run) | 91.3% | 93.5% | Qwen3.8 2.4T A95B |
| Humanity's Last Exam (Artificial Analysis run) | 34.5% | 42.4% | Qwen3.8 2.4T A95B |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| MathArena | Not measured | 31.6% | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Neither model has a result on 20 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| LiveBench design | 70.7% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| Neither model has a result on the other 8 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Flash Vision Exp | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.5% | Not measured | Only DeepSeek V4 Flash Vision Exp measured |
| Long context reasoning (AA-LCR) | 81.3% | 80.3% | DeepSeek V4 Flash Vision Exp |
| Neither model has a result on the other 46 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
DeepSeek V4 Flash Vision Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash Vision Exp costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash Vision Exp costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V4 Flash Vision Exp | Qwen3.8 2.4T A95B |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | August 21, 2026 | August 12, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.44 | $2.00 |
| Output $/Mtok | $1.32 | $6.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash Vision Exp better than Qwen3.8 2.4T A95B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V4 Flash Vision Exp for output tokens 4.5x cheaper ($1.32 vs $6.00 per million) and work measured by AA Coding Index or SciCode, where it leads.
Which is cheaper, DeepSeek V4 Flash Vision Exp or Qwen3.8 2.4T A95B?
DeepSeek V4 Flash Vision Exp costs $0.44 per million input tokens and $1.32 per million output tokens; Qwen3.8 2.4T A95B costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4 Flash Vision Exp or Qwen3.8 2.4T A95B?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4 Flash Vision Exp vs Qwen3.8 2.4T A95B comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Flash Vision Exp's coding index rests on 1 independent benchmark and Qwen3.8 2.4T A95B's on 0. Where only one model has a result, the page says so instead of naming a winner.