GPT-5.1-Codex-Max vs Ling 3.0 Flash
Ling 3.0 Flash costs 82.3x less per output token than GPT-5.1-Codex-Max ($0.1215 vs $10.00 per million). Updated October 22, 2026; 2 benchmarks have a result for both.
GPT-5.1-Codex-Max or Ling 3.0 Flash: which to pick
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; GPT-5.1-Codex-Max has none, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Max for a larger context window (400K against 262K tokens) and work measured by Vibe Code Bench (Vals AI), where it leads.
- Shared results
- 2
- GPT-5.1-Codex-Max only
- 4
- Ling 3.0 Flash only
- 22
- Like-for-like categories
- 1 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
Only Ling 3.0 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Ling 3.0 Flash has a AA Intelligence Index result, 20.1.
Directional only
Agentic work
AA Agentic Index
No call
Only Ling 3.0 Flash has a AA Agentic Index result, 19.3.
Directional only
Reasoning
GPQA Diamond
No call
Only Ling 3.0 Flash has a GPQA Diamond result, 84.8%.
Directional only
Knowledge
MMLU Pro
No call
Only Ling 3.0 Flash has a MMLU Pro result, 82.0%.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
GPT-5.1-Codex-Max $0.0063, Ling 3.0 Flash $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Ling 3.0 Flash has the number, $0.05.
Directional only
At a glance
- Coding index
- GPT-5.1-Codex-Max not benchmarked, Ling 3.0 Flash 29.6 from 2 benchmarks.
- Widest category
- Coding, 9.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.1-Codex-Max 22.2% against Ling 3.0 Flash 2.9%, 19.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Max $10.00 against Ling 3.0 Flash $0.1215 per million output tokens, 82.3x apart.
- Context window
- GPT-5.1-Codex-Max takes 400K tokens, Ling 3.0 Flash 262K.
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 | GPT-5.1-Codex-Max | Bars | Ling 3.0 Flash | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| All shared boards | 1 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 50.6 | Only Ling 3.0 Flash measured |
| LiveCodeBench (Vals AI) | 83.6% | 84.0% | Ling 3.0 Flash |
| SWE-bench Verified (Vals AI) | Not measured | 65.2% | Only Ling 3.0 Flash measured |
| Vibe Code Bench (Vals AI) | 22.2% | 2.9% | GPT-5.1-Codex-Max |
| Code migration (Vals AI) | Not measured | 0.0% | Only Ling 3.0 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 50.2% | Only Ling 3.0 Flash measured |
| SkillsBench (Vals AI) | Not measured | 27.9% | Only Ling 3.0 Flash measured |
| SciCode | Not measured | 42.0% | Only Ling 3.0 Flash 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 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 20.1 | Only Ling 3.0 Flash measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 19.3 | Only Ling 3.0 Flash measured |
| Finance Agent (Vals AI) | Not measured | 30.3% | Only Ling 3.0 Flash measured |
| Legal Research Bench (Vals AI) | Not measured | 0.0% | Only Ling 3.0 Flash measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 25.0% | Only Ling 3.0 Flash measured |
| METR time horizons | 70.8% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 84.8% | Only Ling 3.0 Flash measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 85.5% | Only Ling 3.0 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 23.7% | Only Ling 3.0 Flash measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash | Leader |
|---|---|---|---|
| ProofBench | 9.0% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 82.0% | Only Ling 3.0 Flash measured |
| LegalBench (Vals AI) | Not measured | 79.7% | Only Ling 3.0 Flash measured |
| CorpFin (Vals AI) | Not measured | 61.9% | Only Ling 3.0 Flash measured |
| TaxEval (Vals AI) | Not measured | 70.7% | Only Ling 3.0 Flash measured |
| MedCode (Vals AI) | Not measured | 32.3% | Only Ling 3.0 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 50.7% | Only Ling 3.0 Flash measured |
| MedScribe (Vals AI) | Not measured | 80.9% | Only Ling 3.0 Flash measured |
| Long context reasoning (AA-LCR) | Not measured | 73.0% | Only Ling 3.0 Flash measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Ling 3.0 Flash | 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 costs less
Repository review
50K input + 3K output tokens
Ling 3.0 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.0 Flash 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 |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | No public weights listed | Open weights |
| Released | December 4, 2025 | July 23, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.25 | $0.0405 |
| Output $/Mtok | $10.00 | $0.1215 |
| Coding index | Not benchmarked | 29.6 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | $0.05 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Max better than Ling 3.0 Flash for coding?
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; GPT-5.1-Codex-Max has none, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Max for a larger context window (400K against 262K tokens) and work measured by Vibe Code Bench (Vals AI), where it leads.
Which is cheaper, GPT-5.1-Codex-Max or Ling 3.0 Flash?
GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 per million output tokens; Ling 3.0 Flash costs $0.0405 and $0.1215. Cost per solved task is known for Ling 3.0 Flash only.
Which has the bigger context window, GPT-5.1-Codex-Max or Ling 3.0 Flash?
GPT-5.1-Codex-Max, with 400K tokens against 262K.
How many benchmarks is the GPT-5.1-Codex-Max vs Ling 3.0 Flash comparison based on?
2 benchmarks have a result for both models as of October 22, 2026. GPT-5.1-Codex-Max's coding index rests on 1 independent benchmark and Ling 3.0 Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.