Quick Answer: What Is the Best Affordable LLM for Coding?
DeepSeek V4 Flash is our best overall value pick. Its independently recorded first-party API price is exceptionally low, while its Terminal-Bench 2.1 result remains competitive with models that cost substantially more.
If you prefer a closed model with clearer first-party pricing, GPT-5.6 Luna is the safer low-cost default. For large repositories, MiniMax M3 combines a 1M-token context window with inexpensive cached input.
Here are the category winners:
| Category |
Recommended model |
| Best overall value |
DeepSeek V4 Flash |
| Best low-cost closed model |
GPT-5.6 Luna |
| Best affordable 1M-context model |
MiniMax M3 |
| Best balanced open-weight option |
GLM-5.2 |
| Best for agentic and long-horizon coding |
Qwen3.8 Max |
| Best for speed-sensitive coding |
Gemini 3.6 Flash |
| Best raw coding performance before premium pricing |
Kimi K3 |
| Best premium fallback |
Claude Sonnet 5 |
The short version: start with an inexpensive model, measure whether it completes the task, and escalate only when the task demands it.
Developers comparing providers rather than individual models can also read our guide to the best AI APIs for developers.
Affordable Coding LLM Pricing Comparison
We used two simulated workloads to make the prices easier to compare.
The first represents a small code request:
10,000 input tokens
2,000 output tokens
No cached input
The second represents a repository-level task:
60,000 uncached input tokens
140,000 cached input tokens
20,000 output tokens
The estimated cost is:
Uncached input cost + cached input cost + output cost
These estimates do not include cache-write charges, cache storage, failed attempts, extra tool loops, or differences in reasoning-token usage. They show what the same token workload would cost—not what every coding task will cost in production.
| Rank |
Model |
Input / cached / output per 1M tokens |
Small request |
Repository task |
Terminal-Bench 2.1 |
Best use |
| 1 |
DeepSeek V4 Flash |
$0.14 / $0.0028 / $0.28 |
$0.0020 |
$0.0144 |
78.65% |
Low-cost coding agents and everyday debugging |
| 2 |
GPT-5.6 Luna |
$0.20 / $0.02 / $1.20 |
$0.0044 |
$0.0388 |
80.90% |
High-volume closed-model workloads |
| 3 |
MiniMax M3 |
$0.30 / $0.06 / $1.20 |
$0.0054 |
$0.0504 |
65.17% |
Large repositories on a limited budget |
| 4 |
GLM-5.2 |
$1.40 / $0.26 / $4.40 |
$0.0228 |
$0.2084 |
77.90% |
Multi-file changes and longer agent loops |
| 5 |
Qwen3.8 Max |
$2.00 / $0.25 / $6.00 |
$0.0320 |
$0.2750 |
81.27% |
Agentic and long-horizon coding |
| 6 |
Gemini 3.6 Flash |
$1.50 / $0.15 / $7.50 |
$0.0300 |
$0.2610 |
77.53% |
Fast interactive development |
| 7 |
Kimi K3 |
$3.00 / $0.30 / $15.00 |
$0.0600 |
$0.5220 |
85.02% |
Difficult tasks where failed attempts cost more |
| Baseline |
Claude Sonnet 5 |
$2.00 / $0.20 / $10.00 |
$0.0400 |
$0.3480 |
80.52% |
Premium reliability fallback |
One result deserves attention: Gemini 3.6 Flash has a lower estimated repository bill than Qwen3.8 Max despite its higher output price. That happens because the simulated workload contains much more cached input than output.
Change the workload and the order can change. Output-heavy code generation favors models with cheaper completion tokens; repository agents that repeatedly read the same files benefit more from cache discounts.
How We Ranked These Budget Coding LLMs
Coding Ability Came Before Token Price
We did not allow price alone to determine the ranking.
An extremely small model can generate functions, documentation, and boilerplate for fractions of a cent. That does not make it a good autonomous coding model. Repository work also requires the model to inspect files, use tools, preserve constraints, execute tests, interpret failures, and revise the patch.
We therefore used independent coding and agent evaluations as an ability filter, including:
Terminal-Bench 2.1 for terminal-based agent tasks
SciCode for structured scientific code generation
Broader agentic results as supporting evidence
Scores were taken from the corresponding Artificial Analysis model evaluations, including DeepSeek V4 Flash, GPT-5.6 Luna, MiniMax M3, GLM-5.2, Qwen3.8 Max, Gemini 3.6 Flash, and Kimi K3.
These scores are useful, but they are not interchangeable with every SWE-bench result published elsewhere. Different benchmark versions, scaffolds, tool permissions, and evaluation harnesses can produce different outcomes.
Benchmark performance is evidence. It is not a guarantee that a model will understand your repository.
Token Price Is Not Task Price
Suppose Model A costs $0.02 per attempt and succeeds on its third run. The completed task costs $0.06.
Model B costs $0.04 per attempt but succeeds immediately. It is twice as expensive per run and still one-third cheaper per completed task.
That simple example leaves out an even bigger cost: developer time. A failed migration can require manual review, reverted changes, another prompt, and another test cycle.
The practical metric is therefore:
Cost per accepted task = total API spend across attempts ÷ number of tasks that pass review
Most teams cannot calculate this from a public price table. They need to log task type, token usage, retries, test results, and whether the final change was accepted.
1. DeepSeek V4 Flash — Best Overall Value
DeepSeek V4 Flash takes first place because its coding ability does not collapse with its price.
Artificial Analysis recorded a 78.65% Terminal-Bench 2.1 score and a 49.88% SciCode score for the evaluated route. Its recorded first-party API rates were $0.14 per million input tokens and $0.28 per million output tokens.
Under our repository workload, that produces an estimated bill of only $0.0144.
That is the strongest price-to-capability combination in this shortlist. We would start with it for:
There is an important caveat. The official public pricing presentation has not always exposed the full rate table as clearly as the independent evaluation page. Model availability and the measured first-party route price should therefore be treated as two separately verified facts.
Our judgment: DeepSeek V4 Flash is the best cheap starting model, but not an automatic choice for high-risk migrations. If it repeatedly fails a complex task, continuing to retry it defeats the reason you selected it.
2. GPT-5.6 Luna — Best Low-Cost Closed Coding Model
GPT-5.6 Luna is the strongest inexpensive closed-model default in this comparison.
Its published standard pricing is:
$0.20 per million input tokens
$0.02 per million cached input tokens
$1.20 per million output tokens
It also recorded an 80.90% Terminal-Bench 2.1 score and a 52.55% SciCode score, placing it above DeepSeek V4 Flash on both evaluations.
Our repository workload costs approximately $0.0388—about 2.7 times the DeepSeek estimate, but still below five cents.
That small absolute difference makes Luna attractive when you value a stronger capability result, predictable closed-model access, or high-volume automation more than the lowest possible bill.
The trade-off appears in output-heavy tasks. Luna’s $1.20 output price is more than four times DeepSeek V4 Flash’s recorded $0.28 rate. Long explanations, large patches, and repeated reasoning loops narrow the value gap.
Our judgment: choose Luna when you want a low-cost default with fewer capability compromises; choose DeepSeek when absolute API cost is the priority.
3. MiniMax M3 — Best Cheap LLM for Large Repositories
MiniMax M3 combines three useful numbers:
$0.30 per million input tokens
$0.06 per million cached-input tokens
A 1M-token context window
That makes it inexpensive to feed large amounts of code into the model, especially when repeated agent calls can reuse cached repository context.
The estimated cost of our repository workload is $0.0504. That is higher than Luna, but far below GLM-5.2, Qwen3.8 Max, Gemini 3.6 Flash, and Kimi K3.
The ability results are less impressive. MiniMax M3 recorded 65.17% on Terminal-Bench 2.1 and 45.37% on SciCode, the lowest results among the seven ranked models.
This does not make it useless. It changes where we would deploy it.
MiniMax M3 is a sensible choice for:
Reading and summarizing large repositories
Generating documentation
Writing unit tests
Producing boilerplate
Explaining unfamiliar modules
Moderate debugging with automatic validation
It is a weaker default for autonomous architecture changes or migrations spanning many interdependent files.
Our judgment: MiniMax M3 is a context-value winner, not the capability winner.
4. GLM-5.2 — Best Balanced Open-Weight Coding Model
GLM-5.2 sits between the budget leaders and the more expensive agent-focused models.
Its pricing is $1.40 per million input tokens, $0.26 for cached input, and $4.40 for output. Using our workload, the estimated repository-task cost is $0.2084.
That is roughly four times the MiniMax M3 estimate. The reason to pay more is stronger agent execution: GLM-5.2 recorded 77.90% on Terminal-Bench 2.1, compared with MiniMax M3’s 65.17%.
We would consider GLM-5.2 for:
Multi-file bug fixes
Longer tool-using workflows
Repository refactoring
Tasks that require more planning than boilerplate generation
Teams that prefer an open-weight model family
Its position is slightly awkward. DeepSeek and Luna cost less, while Qwen3.8 Max posts a stronger Terminal-Bench result. GLM-5.2 earns its place by offering a more balanced middle tier.
Our judgment: use GLM-5.2 when MiniMax M3 is not reliable enough but you are not ready to pay for Qwen3.8 Max or Kimi K3.
5. Qwen3.8 Max — Best for Agentic and Long-Horizon Coding
Qwen3.8 Max is not one of the cheapest models in the table. It ranks because its coding results remain strong enough to justify an escalation from the budget tier.
The evaluated route recorded:
$2.00 per million input tokens
$0.25 per million cached-input tokens
$6.00 per million output tokens
81.27% on Terminal-Bench 2.1
52.89% on SciCode
Our repository workload costs approximately $0.2750.
That is nearly 20 times the DeepSeek V4 Flash estimate. But this comparison assumes both models finish in one attempt. If a difficult agent task requires several failed low-cost runs, the gap can shrink quickly.
Qwen3.8 Max is better suited to:
Multi-stage coding agents
Long-horizon development tasks
Complex repository navigation
Multi-file refactoring
Tool-heavy workflows
Projects that need a larger context window
It is overqualified for routine autocomplete and basic test generation. Paying $6 per million output tokens for low-risk boilerplate makes little sense when several cheaper models can handle it.
Our judgment: Qwen3.8 Max is an escalation model, not the model every request should hit first.
You can review the available model details on the Qwen 3.8 Max API page.
6. Gemini 3.6 Flash — Best for Speed-Sensitive Coding Workloads
The word “Flash” can be misleading if you interpret it as “the cheapest.”
Gemini 3.6 Flash costs $1.50 per million input tokens, $0.15 for cached input, and $7.50 for output. Its output price is higher than DeepSeek V4 Flash, Luna, MiniMax M3, GLM-5.2, and Qwen3.8 Max.
Why include it? Speed.
Gemini 3.6 Flash is a better fit when latency and throughput affect the product experience, such as:
It recorded 77.53% on Terminal-Bench 2.1 and 52.66% on SciCode. Those are credible coding results, though they do not dominate the shortlist.
Our simulated repository task costs $0.2610, slightly less than Qwen3.8 Max because cached input accounts for most of the workload. For output-heavy generation, Gemini’s $7.50 output rate becomes more noticeable.
Our judgment: pick Gemini 3.6 Flash for responsiveness, not because its name implies the lowest bill.
For simple explanations and boilerplate, Gemini 3.5 Flash-Lite may be cheaper. Its lower agent results keep it out of the main ranking.
7. Kimi K3 — Best Raw Coding Performance Before Premium Pricing
Kimi K3 recorded the highest coding results in this shortlist:
It also has the highest price among the seven ranked models: $3 per million input tokens and $15 per million output tokens, with a recorded cached-input rate of $0.30.
The repository workload costs approximately $0.5220—more than 36 times the DeepSeek V4 Flash estimate.
So why is it here?
Because failure has a price too. If a difficult migration requires four low-cost attempts, manual cleanup, and repeated test runs, a stronger model can be the less expensive operational choice even when its token bill is higher.
Kimi K3 is better reserved for:
Difficult repository-wide changes
Long-running coding agents
Architecture work
Complex migrations
Tasks with expensive failure or review cycles
It should not be the default model for every code explanation or unit test.
Our judgment: Kimi K3 is the spend-up option when task failure costs more than tokens.
What About Claude Sonnet 5?
Claude Sonnet 5 is included as a premium reference rather than a ranked budget winner.
Its $2 input, $0.20 cached-input, and $10 output rates produce a $0.3480 estimate for our repository workload. Its 80.52% Terminal-Bench 2.1 result remains competitive, but several ranked models are either cheaper, stronger on the selected benchmarks, or both.
That does not make Claude irrelevant. Production teams may value its behavior on their own repositories, existing evaluation history, or reliability in specific agent frameworks.
The correct way to decide is to run the same private task set across both the budget model and the premium fallback.
Our judgment: Claude Sonnet 5 is a fallback to validate, not a budget label to force onto the model.
Which Affordable Coding Model Should You Choose?
For Autocomplete and Boilerplate
Start with MiniMax M3 or a cheaper Flash-Lite-class model.
These tasks are easy to validate and rarely justify premium agent pricing. Require compilation, linting, or unit tests before accepting the result.
For Everyday Debugging
Choose DeepSeek V4 Flash when cost matters most. Choose GPT-5.6 Luna when you want a stronger low-cost closed-model default.
Both remain inexpensive enough for repeated daily use.
For Large Repository Context
Choose MiniMax M3 when the primary challenge is feeding the model a large amount of code cheaply.
Choose GLM-5.2 when the task also requires stronger terminal execution and multi-step changes.
For Multi-File Refactoring
Start with GLM-5.2. Escalate to Qwen3.8 Max if the task requires longer planning, more tool use, or better repository navigation.
Do not judge the result by whether the patch looks convincing. Run the tests.
For Long-Running Coding Agents
Use DeepSeek V4 Flash for cost-sensitive agent loops with strong automated validation.
Use Qwen3.8 Max when the workflow is complex enough that repeated failures would erase the token savings.
For Difficult Architecture or Migration Tasks
Use Kimi K3 as the higher-capability option in this ranking. Keep Claude Sonnet 5 as a premium comparison or fallback.
These tasks should still require human review. A benchmark score is not permission to merge an unsupervised migration.
A Better Strategy: Route Coding Tasks by Difficulty
Choosing one LLM for every coding task is convenient. It is rarely the most economical setup.
A better production strategy uses at least three levels:
Budget route: Send documentation, tests, boilerplate, and routine fixes to DeepSeek V4 Flash, GPT-5.6 Luna, or MiniMax M3.
Escalation route: Send failed or more complex repository tasks to GLM-5.2 or Qwen3.8 Max.
Premium route: Reserve Kimi K3 or Claude Sonnet 5 for migrations, architecture work, and tasks with a high failure cost.
The escalation rule should be measurable. For example, route a task upward when:
The patch fails its test suite
The model exceeds the allowed number of attempts
It modifies files outside the permitted scope
Static analysis finds a new error
A reviewer rejects the patch
The confidence or risk classifier crosses a defined threshold
This structure avoids paying premium prices for easy work without trapping hard work in an endless series of cheap failures.
An all-in-one API can simplify this setup because the application does not need a separate billing account and integration for every model. The value is not merely “many models with one key.” It is the ability to change the route after you collect real cost and acceptance data.
How to Call a Coding Model Through GPT Proto
The example below uses GPT Proto’s OpenAI-compatible chat-completions surface and Qwen3.8 Max.
curl https://api.gptproto.com/v1/chat/completions \
-H "Authorization: Bearer $GPTPROTO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "QWEN_3_8_MAX",
"messages": [
{
"role": "system",
"content": "You are a coding assistant. Make the smallest safe change, explain the cause briefly, and include tests."
},
{
"role": "user",
"content": "Fix the race condition in the provided worker queue without changing its public API."
}
]
}'
For production use, record the usage fields returned by the API together with:
Token usage alone tells you the API bill. Acceptance data tells you whether the model was actually economical.
Final Verdict
DeepSeek V4 Flash is the best overall value coding LLM in this comparison. It combines extremely low recorded API pricing with coding results that remain close to far more expensive models.
GPT-5.6 Luna is the better low-cost closed-model default. It costs more than DeepSeek V4 Flash, but its independent coding results are stronger while the estimated repository bill remains below five cents.
MiniMax M3 is the budget choice for large-context repository reading, provided the task does not require the strongest autonomous execution.
For more difficult work, GLM-5.2 and Qwen3.8 Max form the practical escalation tier. Kimi K3 becomes worthwhile when failed attempts, developer review, and rework cost more than the token difference.
The most cost-effective AI model for coding is therefore not one permanent winner. It is the least expensive model that can reliably finish your specific task—and a routing system that knows when to stop retrying it.
