Ling 3.0 Flash VL vs Mixtral 8x22B Instruct
Ling 3.0 Flash VL leads Mixtral 8x22B Instruct on the Artificial Analysis Intelligence Index, 24.6 to 5.7, and Ling 3.0 Flash VL costs 33.3x less per output token ($0.18 vs $6.00 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
Ling 3.0 Flash VL or Mixtral 8x22B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Ling 3.0 Flash VL for output tokens 33.3x cheaper ($0.18 vs $6.00 per million) and a larger context window (131K against 66K tokens).
- Shared results
- 3
- Ling 3.0 Flash VL only
- 4
- Mixtral 8x22B Instruct only
- 5
- Like-for-like categories
- 2 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
Ling 3.0 Flash VL
Ling 3.0 Flash VL leads, 24.6 against 5.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Ling 3.0 Flash VL has a AA Agentic Index result, 28.7.
Directional only
Long documents
Listed context window
Ling 3.0 Flash VL
Ling 3.0 Flash VL takes 131K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
Ling 3.0 Flash VL $0.0001, Mixtral 8x22B Instruct $0.0050 per turn.
List prices
At a glance
- Coding index
- Ling 3.0 Flash VL not benchmarked, Mixtral 8x22B Instruct not benchmarked.
- Widest category
- Reasoning, 35.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Ling 3.0 Flash VL 86.2% against Mixtral 8x22B Instruct 33.2%, 53.0 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash VL $0.18 against Mixtral 8x22B Instruct $6.00 per million output tokens, 33.3x apart.
- Context window
- Ling 3.0 Flash VL takes 131K tokens, Mixtral 8x22B Instruct 66K.
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 | Ling 3.0 Flash VL | Bars | Mixtral 8x22B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | 57.0 | Not measured | Only Ling 3.0 Flash VL measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 14.8% | Only Mixtral 8x22B Instruct measured |
| SciCode | 44.2% | Not measured | Only Ling 3.0 Flash VL measured |
| SciCode | Not measured | 0.0% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash VL | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.6 | 5.7 | Ling 3.0 Flash VL |
| Epoch Capabilities Index | Not measured | 122.0 | Only Mixtral 8x22B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Ling 3.0 Flash VL | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 28.7 | Not measured | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Ling 3.0 Flash VL | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.2% | 33.2% | Ling 3.0 Flash VL |
| Humanity's Last Exam (Artificial Analysis run) | 22.0% | 4.0% | Ling 3.0 Flash VL |
| Neither model has a result on the other 20 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Ling 3.0 Flash VL | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 53.7% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 17 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | 78.3% | Not measured | Only Ling 3.0 Flash VL measured |
| DTBench | Not measured | 55.1% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash VL | Mixtral 8x22B Instruct | 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
Ling 3.0 Flash VL costs less
Repository review
50K input + 3K output tokens
Ling 3.0 Flash VL costs less
Agent loop
200K input + 10K output tokens
Ling 3.0 Flash VL 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 | inclusionAI Ling 3.0 Flash VL | Mistral Mixtral 8x22B Instruct |
|---|---|---|
| Maker | inclusionAI | Mistral AI |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | April 17, 2024 |
| Context window | 131K | 66K |
| Input $/Mtok | $0.06 | $2.00 |
| Output $/Mtok | $0.18 | $6.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.0 Flash VL better than Mixtral 8x22B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Ling 3.0 Flash VL for output tokens 33.3x cheaper ($0.18 vs $6.00 per million) and a larger context window (131K against 66K tokens).
Which is cheaper, Ling 3.0 Flash VL or Mixtral 8x22B Instruct?
Ling 3.0 Flash VL costs $0.06 per million input tokens and $0.18 per million output tokens; Mixtral 8x22B Instruct costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Ling 3.0 Flash VL or Mixtral 8x22B Instruct?
Ling 3.0 Flash VL, with 131K tokens against 66K.
How many benchmarks is the Ling 3.0 Flash VL vs Mixtral 8x22B Instruct comparison based on?
3 benchmarks have a result for both models as of October 22, 2026. Ling 3.0 Flash VL's coding index rests on 0 independent benchmarks and Mixtral 8x22B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.