The Best Value LLMs for Text AI Agents in 2026
The Best Value LLMs for Text AI Agents in 2026
Flagship models win demos. Production text agents win on a different score: correct facts, clean refusals, and a bill you can live with.
We did not rank the smartest models in the catalog. We ranked the ones cheap enough to run all day on customer chat — GPT-5.6 Luna, Gemini Flash / Flash-Lite, Qwen, MiniMax, GPT-4o mini, GPT-5 mini — on real agent prompts and knowledge bases.
The short version:
- Gemini 2.5 Flash-Lite is the value pick. Fastest, cheapest, and the tightest answers.
- GPT-5.6 Luna is the best OpenAI option in this band. Cheap, strong at "we don't sell that," a bit more willing to over-specify.
- GPT-4o mini is still a sane default if you want OpenAI and the lowest OpenAI bill.
- Qwen 3.5 397B is fine as a free-tier workhorse. It is not the cheapest once you count tokens, and it talks more than it needs to.
- Qwen Plus truncated too often in this run. Do not put it on a live hotel concierge yet.
This is a text-agent test. Voice, tool-heavy booking flows, and "think for 30 seconds" models are out of scope.
What we actually tested
Lab prompts lie. A model that looks clever on "explain quantum computing" can still invent a pet-python fee for a hotel that never published one.
So we cloned four live hospitality agents (read-only — nothing was written back):
| Agent type | What the prompt + KB actually contain |
|---|---|
| Two-property Cairo hotel | Downtown 4-star vs Giza 5-star, room views, dining, events |
| Historical wedding venues | Named palaces, wedding packages in EGP, guest counts |
| 4-star downtown hotel | Museum walking distance, pool policy, facilities |
| Desert / Red Sea camp | Sokhna cabin rates, cancellation rules, pet fee |
Each agent kept its real system prompt and real knowledge base search. We only swapped the model. Invoice, payment, and calendar tools were stripped so a test turn could not charge a guest or write a booking.
9 models × 4 agents × 3 questions = 108 turns.
The three questions per agent were always the same shape:
- A fact the prompt or KB should know (rates, distance, room features)
- A policy question (pool + kids, cancellation + pets)
- A trap — Tokyo suites, Maldives villas, Swiss ski chalets, exotic-animal euro fees — that the business does not sell
A separate judge model scored groundedness, character, helpfulness, and whether the trap was refused without inventing inventory. Scores are 0–10. Cost is provider USD from measured tokens, not list-price guesses.
Honest limits
- This is hospitality chat, not legal research or code.
- The judge had a short expected-fact list. A model that pulled extra KB detail (pool depth, rooftop, heated water) sometimes got tagged as "hallucination" even when that text likely sat in the knowledge base. Concise models look safer. Verbose models look sloppier. We call that out below instead of pretending the 10.0 is gospel.
- Turns were single-shot. No multi-turn memory test.
- We did not test Claude Haiku, GPT-5.6 full, Gemini Pro, or Opus. Those are not the value band.
The scoreboard
| Model | Plan tier | Input / output per 1M | Avg score | Hallucination flag | Trap pass | Avg latency | Cost / turn |
|---|---|---|---|---|---|---|---|
| Gemini 2.5 Flash-Lite | Free | $0.10 / $0.40 | 10.00 | 0% | 100% | 4.2s | $0.00078 |
| Gemini 2.5 Flash | Starter | $0.30 / $2.50 | 8.83 | 17% | 100% | 4.8s | $0.00278 |
| Gemini 3.1 Flash-Lite | Starter | $0.25 / $1.50 | 8.58 | 25% | 75% | 4.6s | $0.00217 |
| GPT-5 mini | Starter | $0.25 / $2.00 | 8.50 | 17% | 75% | 7.3s | $0.00216 |
| GPT-5.6 Luna | Pro | $0.20 / $1.20 | 8.42 | 25% | 100% | 6.3s | $0.00154 |
| GPT-4o mini | Free | $0.15 / $0.60 | 8.25 | 25% | 100% | 5.4s | $0.00104 |
| MiniMax-M3 | Free | $0.30 / $1.20 | 8.25 | 8% | 100% | 8.6s | $0.00259 |
| Qwen 3.5 397B | Free | $0.60 / $3.60 | 7.58 | 42% | 100% | 6.4s | $0.00506 |
| Qwen Plus | Starter | $0.40 / $1.20 | 6.83 | 0%* | 100% | 13.1s | $0.00354 |
*Qwen Plus was not inventing as much as it was cutting off mid-sentence. The judge called that "not hallucinated." Customers will call it broken.
Typical turn size in this set: about 6,600–7,200 input tokens (long hotel prompts + KB snippets) and 80–250 output tokens. That is what a real concierge agent looks like. It is not a 200-token playground chat.
What 10,000 conversations actually cost
Assume 10,000 chats a month and two model turns each (greeting + answer). That is 20,000 turns at the token mix we measured.
| Model | Monthly provider cost | vs Flash-Lite |
|---|---|---|
| Gemini 2.5 Flash-Lite | $16 | — |
| GPT-4o mini | $21 | +31% |
| GPT-5.6 Luna | $31 | +94% |
| GPT-5 mini | $43 | +2.7× |
| Gemini 3.1 Flash-Lite | $43 | +2.8× |
| MiniMax-M3 | $52 | +3.3× |
| Gemini 2.5 Flash | $56 | +3.6× |
| Qwen Plus | $71 | +4.5× |
| Qwen 3.5 397B | $101 | +6.5× |
Qwen 3.5 is the default free model on ConvoCore for a reason: it is capable and unlocked. It is not the cheap model once the prompt is 7,000 tokens. Gemini Flash-Lite and GPT-4o mini eat that context for a fraction of the price.
If you are an agency running 20 client agents at this volume, the gap between Flash-Lite and Qwen 3.5 is about $1,700/month in raw model spend. That is before markup.
Model by model
Gemini 2.5 Flash-Lite — the value winner
Use it for: FAQ, hours, rates, "are kids allowed in the pool," anything where a short correct answer is the product.
It was the only model that scored a 10 on every turn in this set. That is partly skill and partly style. Flash-Lite answers were short and conservative:
"Yes, all Deluxe Rooms at Steigenberger Pyramids Cairo feature a private balcony or terrace."
"A double cabin in Sokhna on a weekday from February 2026 is 4000 EGP."
"I'm sorry, but we do not allow pets at Mar Charbel Hotel Cairo. Therefore, there is no exotic animal hygiene fee."
No invented euro fees. No Tokyo inventory. No 400-word room catalog.
Pros
- Cheapest tokens in the set ($0.10 / $0.40 per 1M)
- Fastest (4.2s average)
- Stays on the question
- Perfect trap record here
Cons
- Will under-sell if the prompt expects a rich upsell or a visual carousel
- Newer Gemini 3.1 Flash-Lite did not beat it on this job
- Google tool-calling in our text runtime is still the weaker path; this test leaned on automatic KB injection, which is how most FAQ turns actually run
Verdict: Default for high-volume text agents unless you have a reason not to.
GPT-5.6 Luna — best OpenAI value
Luna is OpenAI's cheap/fast GPT-5.6 tier ($0.20 / $1.20). It is Pro-gated on ConvoCore, still far below GPT-5.6 full.
It was excellent at traps (10/10) and good at the obvious property facts. It lost points when it got specific:
- Listed extra deluxe room view categories the judge did not have on the expected-fact card
- Quoted a 70 cm rooftop pool, heated water, and poolside drinks on the kids-in-the-pool question
- Missed the cheapest wedding add-on (Katb Ketab at 50,000 EGP) and led with the 85,000 EGP full wedding package
Some of that extra detail is probably in the KB. The judge still flagged it. The production lesson is the same either way: Luna fills in. That is useful for a sales concierge and dangerous if your KB is messy.
On the Tokyo trap it did the right thing: we only cover two Cairo hotels, no JPY presidential rate.
Pros
- OpenAI quality at roughly 6× cheaper input than GPT-5.6
- Strong refusals
- ~$31/month at 10k chats, not $200
- 1M+ context if you ever need it
Cons
- More specific than the source of truth sometimes
- Slower than Gemini Flash-Lite (~6.3s)
- Pro plan, not free
Verdict: Best pick if the client wants OpenAI on the badge and you still care about margin.
GPT-4o mini — the boring one that still works
Lowest OpenAI bill in the set. Answers were short. Trap handling was mixed: it refused the python, then invented a hard "no pets" policy the prompt never stated.
Fact score (7.50) lagged GPT-5 mini. Policy score (10) was clean. Latency 5.4s.
Pros: cheap, familiar, free-tier, low output verbosity (79 tokens average — the lowest). Cons: will state a policy with too much confidence when the KB is silent. Verdict: Fine for simple FAQ agents. Prefer Luna or Flash-Lite if the KB is large.
GPT-5 mini — smarter facts, leakier traps
Best fact score among OpenAI models (9.33). It named balcony types, 36 sqm deluxe rooms, and package names more completely than Luna.
Then it failed the python trap by inventing a $50 USD exotic-animal hygiene fee and converting it to euros. That is the failure mode you cannot ship.
Pros: strong retrieval of room/package detail. Cons: 7.3s, 2× Luna's output price, will fabricate a fee rather than say "I don't know." Verdict: Use for internal drafts or rich sales copy. Not for policy questions until you add a hard "if it is not in the KB, do not price it" rule.
Gemini 2.5 Flash — almost Flash-Lite, triple the bill
Quality close to Flash-Lite (8.83) with more layout chrome (cards, image placeholders). Cost 3.6× Flash-Lite. Trap pass 100%. Same 70 cm pool-depth flag as several other models.
Verdict: Skip it for text FAQ. Spend the extra on a better prompt, not a bigger Flash.
Gemini 3.1 Flash-Lite — newer is not automatically better
On paper this should replace 2.5 Flash-Lite. In this run it did not. Score 8.58, 25% hallucination flags, 75% trap pass. It invented a no-python policy and a 13th-floor pool, then wrapped a room carousel around the refusal.
It was still fast and still cheap. It was not the upgrade.
Verdict: Keep 2.5 Flash-Lite as the production value Gemini until 3.1 stops embroidering.
MiniMax-M3 — capable, wordy, slower
Free-tier, 8.25 average, only 8% hallucination flags, perfect traps. It wrote the cleanest "I do not have a published exotic-animal policy, ask the front desk" refusal in the set.
It also wrote the longest answers (479 output tokens average) and the second-slowest latency (8.6s). One deluxe-room answer died after "Great question! The answer depends on which."
Verdict: Usable on free workspaces. Trim max tokens. Not the cost or speed winner.
Qwen 3.5 397B — the default that costs more than people think
This is ConvoCore's default agent model. It followed character, refused traps, and often had the right number (4000 EGP weekday double).
It also had the highest hallucination flag rate (42%) and the highest cost per turn ($0.005). It listed museum-view deluxe SKUs, 70 cm pools, heated water, and 13th-floor rooftops — the verbose-KB pattern. Input is $0.60 / 1M. On a 7,000-token hotel prompt that adds up fast.
Pros: unlocked on free, good enough, solid trap refusals. Cons: 6.5× Flash-Lite at this token mix; talks past the question. Verdict: Keep it as the onboarding default. Switch production hospitality agents to Flash-Lite or Luna once you care about the invoice.
Qwen Plus — do not ship it like this
Lowest score (6.83). Average latency 13 seconds. Multiple answers ended mid-clause:
"Yes, the majority of Deluxe room categories at Steigenberger Pyramids Cairo feature a private balcony or terrace: - **"
"Starting from February 2026, the rate for a Double Persons Cabin at Dayra Camp S"
The judge marked hallucination 0% because it did not invent. It also did not finish. That is worse.
Verdict: Skip for live text agents until truncation is gone.
What the traps taught us
Every model except GPT-5 mini and Gemini 3.1 Flash-Lite refused the fake inventory cleanly.
The failures were specific:
- GPT-5 mini invented a $50 exotic-animal fee and a euro conversion.
- Gemini 3.1 Flash-Lite invented a python ban, then tried to sell rooms anyway.
The wins were specific too. Luna, Flash-Lite, Flash, Qwen 3.5, Qwen Plus, MiniMax, and GPT-4o mini all declined Tokyo / Maldives / Alps / python-in-euros without quoting a fake rate.
If you only remember one eval: put a trap in the test set. "What's the weather in Paris" does not tell you if the agent will sell a chalet in Zermatt.
What the fact questions taught us
The hard items were not trivia. They were the jobs these agents are paid to do.
Cheapest wedding package. Several models answered 85,000 EGP (Wedding Package 1) and skipped Katb Ketab at 50,000 EGP. That is a sales miss, not a small hallucination. Gemini 3.1 Flash-Lite was one of the few that named both.
Deluxe balcony. The KB supports "yes, private balcony/terrace, ~36 sqm." Flash-Lite said that in two sentences. Other models enumerated five view categories. If those categories are in the KB, great. If they are a blend of KB + memory, you have a support ticket waiting.
Weekday double at the camp. This number is in the prompt itself (4000 EGP). Almost everyone got it. Qwen Plus died mid-price. That is a runtime bug, not a knowledge bug.
Kids in the pool. The safe answer is: children welcome, supervise them, it is a relaxation pool. Models that added 70 cm / rooftop / 13th floor / heated / drinks may have been reading the KB. The judge punished them. Production takeaway: if a number matters (depth, floor, fee), put it in the prompt, not only in a scraped facilities page.
How to choose
| You are… | Use |
|---|---|
| Running high-volume website / WhatsApp FAQ | Gemini 2.5 Flash-Lite |
| An agency that wants OpenAI on the invoice | GPT-5.6 Luna |
| On the free plan and cannot move yet | GPT-4o mini or Qwen 3.5, then upgrade |
| Selling rooms with a long prompt and a fat KB | Flash-Lite or Luna, not Qwen 3.5, because of input tokens |
| Asking the model to invent a price when the KB is silent | None of these. Fix the prompt. GPT-5 mini will happily make one up. |
| Chasing "the newest Gemini" | Measure it. 3.1 Flash-Lite lost to 2.5 Flash-Lite here. |
A practical setup on ConvoCore:
- Flash-Lite on the public widget and WhatsApp.
- Luna on the one client who insists on OpenAI.
- Keep Qwen 3.5 as the create-agent default so free workspaces boot.
- Do not put Qwen Plus on a concierge until it stops truncating.
- Add two eval questions to every agent: one rate/policy fact, one "do you sell X in another country" trap.
Method, in one paragraph
We loaded four NA-region production agents, projected their live prompts into the same LangGraph text runtime the product uses, attached their real hybrid KB search, overrode only modelId, and ran 12 scripted customer messages per model. Tokens came from the provider usage object. USD used ConvoCore catalog provider prices. The judge was Gemini 3.1 Flash-Lite with a fixed rubric. No agent documents, conversations, or tools were mutated.
If you want the same test on your own agents, the job is boring on purpose: freeze the prompt, freeze the KB, swap the model, keep the traps.
Bottom line
The best value LLM for a text agent in 2026 is not the one that wins a reasoning leaderboard.
It is the one that says 4000 EGP, does not invent a Swiss chalet, and costs sixteen dollars to run 10,000 conversations.
Right now that is Gemini 2.5 Flash-Lite. If you need OpenAI, it is GPT-5.6 Luna. Everything else in this band is either slower, wordier, or more expensive for the same front-desk job.