GPT-5.1-Codex-Max vs Ling 3.0 Flash VL
Ling 3.0 Flash VL costs 55.6x less per output token than GPT-5.1-Codex-Max ($0.18 vs $10.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $10.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.18
GPT-5.1-Codex-Max or Ling 3.0 Flash VL: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Max for a larger context window (400K against 131K tokens).
No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench; Ling 3.0 Flash VL on AA Coding Index, SciCode and AA Intelligence Index. Compare them on price and context instead.
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
No call
Only Ling 3.0 Flash VL has a AA Intelligence Index result, 24.6.
Directional only
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
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
GPT-5.1-Codex-Max $0.0063, Ling 3.0 Flash VL $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Max not benchmarked, Ling 3.0 Flash VL not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-5.1-Codex-Max $10.00 against Ling 3.0 Flash VL $0.18 per million output tokens, 55.6x apart.
- Context window
- GPT-5.1-Codex-Max takes 400K tokens, Ling 3.0 Flash VL 131K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards measured
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 57.0 | Only Ling 3.0 Flash VL measured |
| LiveCodeBench (Vals AI) | 83.6% | Not measured | Only GPT-5.1-Codex-Max measured |
| Vibe Code Bench (Vals AI) | 22.2% | Not measured | Only GPT-5.1-Codex-Max measured |
| SciCode | Not measured | 44.2% | Only Ling 3.0 Flash VL measured |
| Terminal-Bench | 60.4% | Not measured | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | 1228.3 | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 24.6 | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 28.7 | Only Ling 3.0 Flash VL measured |
| METR time horizons | 70.8% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 86.2% | Only Ling 3.0 Flash VL measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 22.0% | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| ProofBench | 9.0% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 12 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | Not measured | 78.3% | Only Ling 3.0 Flash VL measured |
| Neither model has a result on the other 45 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 | OpenAI GPT-5.1-Codex-Max | inclusionAI Ling 3.0 Flash VL |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | No public weights listed | Open weights |
| Released | December 4, 2025 | September 10, 2026 |
| Context window | 400K | 131K |
| Input $/Mtok | $1.25 | $0.06 |
| Output $/Mtok | $10.00 | $0.18 |
| 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 GPT-5.1-Codex-Max better than Ling 3.0 Flash VL for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Max for a larger context window (400K against 131K tokens).
Which is cheaper, GPT-5.1-Codex-Max or Ling 3.0 Flash VL?
GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 per million output tokens; Ling 3.0 Flash VL costs $0.06 and $0.18. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Max or Ling 3.0 Flash VL?
GPT-5.1-Codex-Max, with 400K tokens against 131K.
Has any benchmark tested both GPT-5.1-Codex-Max and Ling 3.0 Flash VL?
No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench; Ling 3.0 Flash VL on AA Coding Index, SciCode and AA Intelligence Index. Compare them on price and context instead. Data as of October 22, 2026.