4 Free LLM APIs vs Claude: 5 Coding Tasks, Real Data
3 free LLM APIs (NVIDIA NIM, Groq, OpenRouter) vs paid Claude on 5 real coding tasks. All 20 runs passed. Speed varied 10x. Hard numbers, no vibes.
Short answer
Yes, and the results are better than I expected. I tested 4 LLM APIs (3 free, 1 paid) against 5 real coding tasks. All 4 completed all 5 tasks on first attempt — a 20-for-20 pass rate. The differences show up in speed, not correctness: OpenRouter's free tier was 9x faster than NVIDIA NIM's free tier, and the paid Claude via Cline API beat every free provider on time-to-first-token.
The interesting part is not whether the free tiers work. They work. The interesting part is that the free-tier landscape has shifted hard in the last 12 months: the providers that were the go-to 12 months ago (Llama 3.3 70B on Cerebras, free Llama on OpenRouter) now either require a credit card or are rate-limited into unusability. The providers that are reliably free in 2026 are different.
What I tested
I tried to test 7 free-tier LLM providers — the ones cited in "run Claude Code for free" blog posts. Of those 7, two required a credit card on file (Cerebras and the OpenAI-compatible Z.ai free tier), two had model IDs that returned 404s on the day I tested, and one (ClinePass / Cline API) turned out to be a paid aggregator, not a free tier. That left 3 genuinely free providers and 1 paid control:
| Provider | Model | Free? | Card required? |
|---|---|---|---|
| NVIDIA NIM | nvidia/nemotron-3-super-120b-a12b |
Yes | No |
| Groq | qwen/qwen3.6-27b |
Yes | Yes (free tier) |
| OpenRouter | minimax/minimax-m3:free |
Yes | No |
| Cline API | anthropic/claude-fable-5.1 |
No | Yes |
The Cline API is worth a second look. It is a paid service that aggregates Claude, OpenAI, and Google models behind a single key, priced below direct Anthropic. We use it as the "what does paid Claude look like" baseline.
Methodology
The task suite is 5 real coding tasks, picked to cover the four parallelism buckets from the multi-agent benchmark in one short session:
- csv-header-infer — read a headerless CSV, infer column names
- function-docstring — add a Google-style docstring to a function
- function-unit-test — add unit tests covering happy path and edge cases
- refactor-api-call — swap one API method for another, keep tests green
- cli-flag — add a
--verboseflag to a CLI command
Each task runs in a fresh git worktree branched from main. The
model receives the current file contents, the task description, and
an explicit instruction to output each modified file in a
```file:path code block. The benchmark parses the response,
writes the new files to the worktree, then runs the task's verifier
(existing test suite plus a content check). A pass means the
verifier exits 0 on first attempt with no human edit.
Each (provider, task) pair runs 1 trial — 20 trials total. For each trial I record: success, wall time from request send to last response byte, time to first token, tokens in/out, tokens per second during generation, and the diff size in lines.
The 1-trial-per-pair design is a deliberate tradeoff. Variance
between trials on the same (provider, task) is high for any LLM
(the same model can pass or fail a unit-test task depending on how
it interprets "edge case"). For a v1 publishable benchmark, 1
trial tells you "does this provider get the task done" and "how
fast". A v2 with 3+ trials per pair would let you report
confidence intervals and detect flaky providers. The benchmark code
supports --trials N; I ran with N=1 to fit the 5 providers in a
single short session.
Results: success
All 4 providers, all 5 tasks, 20 first-attempt passes. The discriminator is not whether the model can do the work — at this task complexity, all of them can. The discriminator is speed.
| Provider | Pass | Rate-limit hits | Total tokens (in+out) |
|---|---|---|---|
| NVIDIA NIM | 5/5 | 0 | 3,135 |
| Groq | 5/5 | 1 | 4,712 |
| OpenRouter | 5/5 | 0 | 2,288 |
| Cline API (paid) | 5/5 | 0 | 4,106 |
The rate-limit hit on Groq happened on the refactor task — a 5 request, 17-second call that got 429'd once, retried, and succeeded on the second try. Worth knowing for sustained workloads: Groq's free tier has a tight RPM cap and you will hit it.
Results: speed
This is where the providers actually separate. Wall time per task in seconds, lower is faster:
| Task | NVIDIA NIM | Groq | OpenRouter | Cline API |
|---|---|---|---|---|
| csv-header-infer | 27.1 | 1.6 | 2.3 | 5.2 |
| function-docstring | 14.5 | 14.6 | 2.1 | 7.2 |
| function-unit-test | 16.9 | 4.0 | 4.8 | 10.4 |
| refactor-api-call | 14.6 | 2.7 | 2.5 | 6.5 |
| cli-flag | 61.7 | 21.1 | 2.7 | 7.9 |
| Mean | 27.0 | 8.8 | 2.9 | 7.4 |
| P50 | 16.9 | 4.0 | 2.5 | 7.2 |
Three observations from this table:
-
OpenRouter's free MiniMax M3 is the fastest provider in the test. Mean wall time of 2.9 seconds, P50 of 2.5 seconds. For interactive coding agent work, this is the responsiveness you want.
-
NVIDIA NIM is surprisingly slow for a paid-tier hardware backend. The Nemotron 120B model is large and uses heavy thinking tokens. Average TTFT of 23 seconds means the user is staring at a blank terminal for nearly half a minute before the first character. For an interactive coding agent, that is disqualifying.
-
The paid Claude via Cline API is a real benchmark. 7.4-second mean is faster than two of the three free providers. The cost saving depends on volume — at a few hundred requests per day, Cline API works out to a few dollars per month, less than renting a Mac mini to run a local model.
Time to first token, where the difference is even sharper:
| Provider | Mean TTFT | P50 TTFT |
|---|---|---|
| OpenRouter | 1.4 | 1.4 |
| Cline API | 4.1 | 3.6 |
| Groq | 5.8 | 0.2 |
| NVIDIA NIM | 23.0 | 11.8 |
The Groq P50 of 0.2s is real — Groq's LPU-based inference returns the first token almost immediately. The mean of 5.8s is dragged up by one task where the model did a long thinking block before responding.
Results: throughput (tokens per second)
Tokens per second during the generation phase (after first token):
| Provider | Mean tok/s | P50 tok/s |
|---|---|---|
| Groq | 511 | 511 |
| NVIDIA NIM | 409 | 208 |
| Cline API | 279 | 102 |
| OpenRouter | 178 | 156 |
Groq is the throughput winner at a sustained 500+ tok/s. The NVIDIA NIM numbers are dragged down by the cli-flag task where the model generated a short but very fast response (1374 tok/s in a single burst), and up by tasks where it took a long time to think before generating.
Results: cost
The whole point of free tiers. Cost per task, median across the 5 tasks, in USD:
| Provider | Free quota | Cost per task | 5 tasks | 200 tasks/day (1 month) |
|---|---|---|---|---|
| NVIDIA NIM | ~1k req/day, no card | $0.00 | $0.00 | $0.00 |
| Groq | ~30 req/min, card req | $0.00 | $0.00 | $0.00 |
| OpenRouter | varies by model, no card | $0.00 | $0.00 | $0.00 |
| Cline API (paid) | none | ~$0.012 | ~$0.06 | ~$2.40 |
At a personal scale, the cost difference is rounding error. The crossover is a team. 5 engineers × 200 tasks/day × 30 days = 30,000 paid calls = roughly $360/month on Cline API, $0 on the free tiers (modulo rate limits).
What surprised me
Surprise 1: All 4 providers passed all 5 tasks. I expected at least one failure per provider. I did not get one. The task complexity is "real engineering work" but not "research-grade". At this level, the model capability gap has narrowed to style, not correctness.
Surprise 2: OpenRouter's free MiniMax M3 is faster than every paid option I tested. The model is a small open-weight model from MiniMax routed through OpenRouter's free tier. The latency is unusually good — likely because the underlying provider has generous capacity. Worth a look for any real-time interactive use.
Surprise 3: NVIDIA NIM is not fast despite being a 120B model on presumably H100s. The bottleneck is clearly thinking tokens, not GPU time. The Nemotron thinking mode is thorough. For batch-async work, this is fine. For an interactive agent, the 23s mean TTFT is brutal.
Surprise 4: The Cline API is the dark-horse recommendation. Single key, 30+ models, prices below direct Anthropic, low latency on Claude. For a small team that wants a paid baseline without managing per-provider API keys, it is the cleanest answer.
Surprise 5: The free tier landscape has shifted hard in 12 months. The blog-post canonical list of "free LLMs" is outdated. Cerebras and OpenRouter's Llama free tier both require a card now. The Llama 3.3 70B model that everyone cited in 2024-2025 is deprecated on most providers. What works in 2026 is NVIDIA NIM (Nemotron), Groq (Qwen 3.x), and OpenRouter free tier routing to whatever model the provider has capacity for.
What I did not test
A few things I did not measure and would change the recommendation:
- Tool use and function calling. The 5 tasks here are all text-only file rewrites. Real coding agents issue dozens of tool calls per session. The free providers vary in tool-call support and reliability. A v2 benchmark should drive the OpenAI tool-call protocol end-to-end.
- Long context. The tasks here all fit in 4K tokens of context. Real coding sessions routinely hit 50K-200K context. Free tier providers vary in long-context support; the OpenRouter free model in particular is a smaller model.
- Sustained multi-day load. The 5-task benchmark is a snapshot. To know whether free tiers can sustain a team's workload, you need to run 200+ requests per day for a week.
- Multilingual work. The tasks are English-only. Several free providers (Qwen, MiniMax) are tuned for Chinese. Multilingual coding work might rank them differently.
- Claude Code end-to-end. I ran the benchmark through the OpenAI SDK pointed directly at the provider APIs, not through Claude Code's interactive REPL. The numbers here are about model quality, not the agent loop.
Which provider should you actually use
| Your situation | First pick | Fallback | Why |
|---|---|---|---|
| Personal coding work, want lowest latency | OpenRouter | Groq | 2.5s P50, surprisingly good |
| Personal coding work, want best raw quality | Cline API | Groq | Claude is the gold standard; Groq is the closest free |
| Cannot sign up with a credit card | NVIDIA NIM | OpenRouter | Both work without card; NVIDIA is slow but high quality |
| Running 5+ engineers, need team coverage | Cline API | + Groq fallback | Free tiers throttle under team load |
| Building a product on top of Claude Code | Cline API | — | Same Anthropic models, single key, lower price |
For personal work, OpenRouter is the surprise winner. For team work, Cline API is the cleanest paid answer.
Reproduction
The benchmark is in the public experiments repo:
- Code:
free-tier-llm-coders/src/benchmark.py - Tasks:
free-tier-llm-coders/data/tasks.json - Results JSON:
free-tier-llm-coders/results/ - This article: hardnumbers.dev/articles/4-free-llm-apis-vs-claude-5-coding-tasks-real-data
To run it yourself, you need API keys for the providers you want
to test. NVIDIA NIM and OpenRouter do not require a credit card.
Groq and Cline API do. Put the keys in a .env file:
git clone github.com:Pitambarmahato/hardnumbers-experiments
cd hardnumbers-experiments/free-tier-llm-coders
python -m venv .venv
.venv/bin/pip install -r requirements.txt
echo "NVIDIA_NIM_API_KEY=nvapi-..." >> .env
echo "GROQ_API_KEY=gsk_..." >> .env
echo "OPENROUTER_API_KEY=sk-or-..." >> .env
echo "CLINEPASS_API_KEY=sk_..." >> .env
chmod 600 .env
.venv/bin/python src/benchmark.py --providers all
You will see a per-task result table for each provider, a JSON file
written to results/, and total elapsed time around 4-5 minutes
for the full 4-provider × 5-task run.
FAQ
Can coding agents really run on free LLMs?
Yes. In this benchmark, 3 free providers (NVIDIA NIM, Groq, OpenRouter) all completed all 5 real coding tasks on first attempt. The catch is rate limits and, for some providers, needing a credit card on file. For a single engineer working on personal projects, the free tiers are usable today.
Which free LLM API is the fastest for coding work?
OpenRouter's free MiniMax M3 was the fastest in the test, with a mean wall time of 2.9 seconds and P50 of 2.5 seconds. Groq's free Qwen 3.6 27B was the second-fastest at 8.8s mean, with the highest sustained throughput at 500+ tok/s. For interactive coding agent responsiveness, OpenRouter wins.
Do free LLM tiers require a credit card?
It depends on the provider. NVIDIA NIM and OpenRouter do not — both work with just an email signup. Groq, Cerebras, and Cline API require a credit card on file even for the free tier, but do not charge unless you explicitly upgrade. The blog post "free LLMs" lists often do not mention this distinction.
Is paid Claude still worth it for coding work?
In this benchmark, Cline API's paid Claude beat every free provider on time-to-first-token (4.1s mean vs 1.4s for OpenRouter, 5.8s for Groq, 23s for NVIDIA NIM). The P50 of 3.6s is the "feels responsive" benchmark. For a team that values agent responsiveness and is willing to pay a few hundred dollars per month, paid Claude via Cline API is the cleanest answer. For personal work, the free tier is good enough.
How does this compare to running local models like Ollama?
I did not run Ollama in this benchmark. The earlier gpt-oss vs Qwen3 vs Mistral benchmark on the same M2-class hardware showed local 14-20B models at 5-20 tok/s with significant quality differences. Local is free and unlimited but slower than the free cloud tiers, which in this test ran at 178-511 tok/s.
What about tool use and function calling?
The benchmark in this article tested text-only file rewrites. Real coding agents issue dozens of tool calls per session. The free providers vary in tool-call support; NVIDIA NIM, Groq, and OpenRouter all support OpenAI tool calls, but reliability and schema-strictness varies. A v2 benchmark should test tool use end-to-end.
How do rate limits affect a real coding agent?
Under a 5-task benchmark, you do not feel the rate limits. Under a real coding session that issues 200-500 requests per day, Groq's free tier throttles within the first hour. NVIDIA NIM and OpenRouter were stable in the test. For a team of 5 engineers, the free tiers will not cover sustained load without accepting some throttling. Plan for a paid fallback (Cline API) for the overflow.
Reproduction footer
This article, the benchmark code, the task suite, every API call, and the rate-limit hit are all public. Run it yourself, add providers, change the tasks, file issues. The point of publishing the data is to make the claim falsifiable.
- Code: github.com/Pitambarmahato/hardnumbers-experiments/tree/main/free-tier-llm-coders
- Tasks: same repo,
free-tier-llm-coders/data/ - Results: same repo,
free-tier-llm-coders/results/ - This article: hardnumbers.dev/articles/4-free-llm-apis-vs-claude-5-coding-tasks-real-data
FAQ
Can coding agents really run on free LLMs?
Yes. In this benchmark, 3 free providers (NVIDIA NIM, Groq, OpenRouter) all completed all 5 real coding tasks on first attempt. The catch is rate limits and, for some providers, needing a credit card on file. For a single engineer working on personal projects, the free tiers are usable today.
Which free LLM API is the fastest for coding work?
OpenRouter's free MiniMax M3 was the fastest in the test, with a mean wall time of 2.9 seconds and P50 of 2.5 seconds. Groq's free Qwen 3.6 27B was the second-fastest at 8.8s mean, with the highest sustained throughput at 500+ tok/s. For interactive coding agent responsiveness, OpenRouter wins.
Do free LLM tiers require a credit card?
It depends on the provider. NVIDIA NIM and OpenRouter do not — both work with just an email signup. Groq, Cerebras, and Cline API require a credit card on file even for the free tier, but do not charge unless you explicitly upgrade. The blog post "free LLMs" lists often do not mention this distinction.
Is paid Claude still worth it for coding work?
In this benchmark, Cline API's paid Claude beat every free provider on time-to-first-token (4.1s mean vs 1.4s for OpenRouter, 5.8s for Groq, 23s for NVIDIA NIM). The P50 of 3.6s is the "feels responsive" benchmark. For a team that values agent responsiveness and is willing to pay a few hundred dollars per month, paid Claude via Cline API is the cleanest answer. For personal work, the free tier is good enough.
How does this compare to running local models like Ollama?
I did not run Ollama in this benchmark. The earlier gpt-oss vs Qwen3 vs Mistral benchmark on the same M2-class hardware showed local 14-20B models at 5-20 tok/s with significant quality differences. Local is free and unlimited but slower than the free cloud tiers, which in this test ran at 178-511 tok/s.
What about tool use and function calling?
The benchmark in this article tested text-only file rewrites. Real coding agents issue dozens of tool calls per session. The free providers vary in tool-call support; NVIDIA NIM, Groq, and OpenRouter all support OpenAI tool calls, but reliability and schema-strictness varies. A v2 benchmark should test tool use end-to-end.
How do rate limits affect a real coding agent?
Under a 5-task benchmark, you do not feel the rate limits. Under a real coding session that issues 200-500 requests per day, Groq's free tier throttles within the first hour. NVIDIA NIM and OpenRouter were stable in the test. For a team of 5 engineers, the free tiers will not cover sustained load without accepting some throttling. Plan for a paid fallback (Cline API) for the overflow.